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  Subjects -> PHARMACY AND PHARMACOLOGY (Total: 575 journals)
Showing 1 - 200 of 253 Journals sorted by number of followers
Nature Reviews Drug Discovery     Full-text available via subscription   (Followers: 352)
Journal of Clinical Oncology     Hybrid Journal   (Followers: 260)
International Journal of Drug Policy     Hybrid Journal   (Followers: 241)
Journal of Medicinal Chemistry     Hybrid Journal   (Followers: 167)
Drugs     Full-text available via subscription   (Followers: 146)
Journal of Pharmaceutical Sciences     Hybrid Journal   (Followers: 140)
Advanced Drug Delivery Reviews     Hybrid Journal   (Followers: 95)
European Journal of Pharmaceutical Sciences     Hybrid Journal   (Followers: 84)
Drug Safety     Full-text available via subscription   (Followers: 81)
Pharmaceutical Research     Hybrid Journal   (Followers: 69)
Drug Discovery Today     Full-text available via subscription   (Followers: 63)
Biomaterials     Hybrid Journal   (Followers: 54)
Annals of Pharmacotherapy     Hybrid Journal   (Followers: 52)
Clinical Pharmacology & Therapeutics     Hybrid Journal   (Followers: 31)
Pharmacoepidemiology and Drug Safety     Hybrid Journal   (Followers: 29)
AAPS Journal     Hybrid Journal   (Followers: 29)
Annual Review of Pharmacology and Toxicology     Full-text available via subscription   (Followers: 27)
Journal of Pain & Palliative Care Pharmacotherapy     Hybrid Journal   (Followers: 26)
Regulatory Toxicology and Pharmacology     Hybrid Journal   (Followers: 26)
British Journal of Clinical Pharmacology     Hybrid Journal   (Followers: 25)
Journal of Controlled Release     Hybrid Journal   (Followers: 25)
Drug Development and Industrial Pharmacy     Hybrid Journal   (Followers: 25)
International Journal of Pharmacy Practice     Full-text available via subscription   (Followers: 24)
European Journal of Pharmaceutics and Biopharmaceutics     Hybrid Journal   (Followers: 23)
International Journal of Pharmaceutics     Hybrid Journal   (Followers: 23)
Journal of Clinical Psychopharmacology     Hybrid Journal   (Followers: 23)
Journal of Pharmacy and Pharmacology     Full-text available via subscription   (Followers: 23)
Critical Reviews in Toxicology     Hybrid Journal   (Followers: 22)
PharmacoEconomics     Full-text available via subscription   (Followers: 21)
American Journal of Cardiovascular Drugs     Hybrid Journal   (Followers: 20)
Chemical Research in Toxicology     Hybrid Journal   (Followers: 20)
Trends in Pharmacological Sciences     Full-text available via subscription   (Followers: 17)
Clinical Toxicology     Hybrid Journal   (Followers: 17)
Clinical Pharmacokinetics     Full-text available via subscription   (Followers: 16)
Critical Reviews in Clinical Laboratory Sciences     Hybrid Journal   (Followers: 16)
Journal of Natural Products     Hybrid Journal   (Followers: 16)
Journal of Pharmacokinetics and Pharmacodynamics     Hybrid Journal   (Followers: 16)
Toxicology and Applied Pharmacology     Hybrid Journal   (Followers: 16)
Pharmaceutical Development and Technology     Hybrid Journal   (Followers: 16)
Journal of Applied Toxicology     Hybrid Journal   (Followers: 15)
Psychopharmacology     Hybrid Journal   (Followers: 15)
Journal of Clinical Pharmacy and Therapeutics     Hybrid Journal   (Followers: 14)
Journal of Oncology Pharmacy Practice     Hybrid Journal   (Followers: 14)
Journal of the American Pharmacists Association     Full-text available via subscription   (Followers: 14)
Toxicology     Hybrid Journal   (Followers: 14)
International Journal of Toxicology     Hybrid Journal   (Followers: 13)
Journal of Pharmaceutical and Biomedical Analysis     Hybrid Journal   (Followers: 13)
Journal of Pharmacy Practice     Hybrid Journal   (Followers: 13)
Biopharmaceutics and Drug Disposition     Hybrid Journal   (Followers: 12)
Cardiovascular Drugs and Therapy     Hybrid Journal   (Followers: 12)
Clinical Trials     Hybrid Journal   (Followers: 12)
Toxicology Letters     Hybrid Journal   (Followers: 12)
Drug and Chemical Toxicology     Hybrid Journal   (Followers: 12)
American Journal of Therapeutics     Hybrid Journal   (Followers: 11)
Basic & Clinical Pharmacology & Toxicology     Hybrid Journal   (Followers: 11)
European Journal of Clinical Pharmacology     Hybrid Journal   (Followers: 11)
Journal of Psychopharmacology     Hybrid Journal   (Followers: 11)
Pharmacy Education     Full-text available via subscription   (Followers: 11)
Clinical Therapeutics     Hybrid Journal   (Followers: 10)
CNS Drugs     Full-text available via subscription   (Followers: 10)
Pharmaceutical Statistics     Hybrid Journal   (Followers: 10)
Seminars in Oncology Nursing     Full-text available via subscription   (Followers: 10)
Journal of Clinical Pharmacology     Hybrid Journal   (Followers: 10)
Toxicological Sciences     Hybrid Journal   (Followers: 10)
Advances in Pharmacological and Pharmaceutical Sciences     Open Access   (Followers: 10)
Biochemical Pharmacology     Hybrid Journal   (Followers: 9)
ChemMedChem     Hybrid Journal   (Followers: 9)
Drug Metabolism and Disposition     Hybrid Journal   (Followers: 9)
Drugs & Aging     Full-text available via subscription   (Followers: 9)
Drugs & Therapy Perspectives     Full-text available via subscription   (Followers: 9)
Medicinal Chemistry     Hybrid Journal   (Followers: 9)
Seminars in Hematology     Hybrid Journal   (Followers: 9)
Current Opinion in Pharmacology     Hybrid Journal   (Followers: 8)
Current Pharmaceutical Biotechnology     Hybrid Journal   (Followers: 8)
Drug Development Research     Hybrid Journal   (Followers: 8)
Epilepsy Research     Hybrid Journal   (Followers: 8)
European Neuropsychopharmacology     Hybrid Journal   (Followers: 8)
Food Additives & Contaminants Part A     Hybrid Journal   (Followers: 8)
Progress in Neuro-Psychopharmacology and Biological Psychiatry     Hybrid Journal   (Followers: 8)
Toxicology in Vitro     Hybrid Journal   (Followers: 8)
Inhalation Toxicology     Hybrid Journal   (Followers: 8)
Antiviral Research     Hybrid Journal   (Followers: 7)
Current Medicinal Chemistry     Hybrid Journal   (Followers: 7)
Drug Delivery     Open Access   (Followers: 7)
Environmental Toxicology and Pharmacology     Hybrid Journal   (Followers: 7)
Experimental and Clinical Psychopharmacology     Full-text available via subscription   (Followers: 7)
Frontiers in Drug Design & Discovery     Hybrid Journal   (Followers: 7)
Journal of Veterinary Pharmacology and Therapeutics     Hybrid Journal   (Followers: 7)
Prescriber     Hybrid Journal   (Followers: 7)
Toxicology Mechanisms and Methods     Hybrid Journal   (Followers: 7)
Journal of Pharmaceutical Innovation     Hybrid Journal   (Followers: 7)
AAPS PharmSciTech     Hybrid Journal   (Followers: 6)
Biometrical Journal     Hybrid Journal   (Followers: 6)
Clinical Drug Investigation     Full-text available via subscription   (Followers: 6)
Current Drug Delivery     Hybrid Journal   (Followers: 6)
Anti-Inflammatory & Anti-Allergy Agents in Medicinal Chemistry     Hybrid Journal   (Followers: 6)
Expert Review of Pharmacoeconomics & Outcomes Research     Full-text available via subscription   (Followers: 6)
Human & Experimental Toxicology     Hybrid Journal   (Followers: 6)
Toxicology and Industrial Health     Hybrid Journal   (Followers: 6)
Current Cancer Therapy Reviews     Hybrid Journal   (Followers: 5)
Current Drug Discovery Technologies     Hybrid Journal   (Followers: 5)
Anti-Infective Agents     Hybrid Journal   (Followers: 5)
Current Therapeutic Research     Open Access   (Followers: 5)
Reviews of Physiology, Biochemistry and Pharmacology     Hybrid Journal   (Followers: 5)
Expert Review of Anti-infective Therapy     Full-text available via subscription   (Followers: 5)
Expert Review of Molecular Diagnostics     Full-text available via subscription   (Followers: 5)
Fitoterapia     Hybrid Journal   (Followers: 5)
Journal of Pain Management & Medicine     Open Access   (Followers: 5)
Journal of Cardiovascular Pharmacology and Therapeutics     Hybrid Journal   (Followers: 5)
Journal of Separation Science     Hybrid Journal   (Followers: 5)
Scandinavian Journal of Clinical and Laboratory Investigation     Hybrid Journal   (Followers: 5)
Clinical Research and Regulatory Affairs     Hybrid Journal   (Followers: 5)
Pharmacogenomics Journal     Hybrid Journal   (Followers: 5)
ASSAY and Drug Development Technologies     Hybrid Journal   (Followers: 4)
BioDrugs     Full-text available via subscription   (Followers: 4)
Cancer Chemotherapy and Pharmacology     Hybrid Journal   (Followers: 4)
Current Pharmaceutical Design     Hybrid Journal   (Followers: 4)
Expert Review of Cardiovascular Therapy     Full-text available via subscription   (Followers: 4)
International Journal of Pharmaceutical and Healthcare Marketing     Hybrid Journal   (Followers: 4)
Pharmaceutical Medicine     Full-text available via subscription   (Followers: 4)
Journal of Child and Adolescent Psychopharmacology     Hybrid Journal   (Followers: 4)
Journal of Infection and Chemotherapy     Hybrid Journal   (Followers: 4)
Journal of Labelled Compounds and Radiopharmaceuticals     Hybrid Journal   (Followers: 4)
Journal of Pharmacology and Experimental Therapeutics     Hybrid Journal   (Followers: 4)
Neuropharmacology     Hybrid Journal   (Followers: 4)
Planta Medica     Hybrid Journal   (Followers: 4)
Immunopharmacology and Immunotoxicology     Hybrid Journal   (Followers: 4)
Physiology International     Full-text available via subscription   (Followers: 3)
Archiv der Pharmazie     Hybrid Journal   (Followers: 3)
BMC Pharmacology     Open Access   (Followers: 3)
Cardiovascular Therapeutics     Open Access   (Followers: 3)
Clinical and Experimental Pharmacology and Physiology     Hybrid Journal   (Followers: 3)
CNS Drug Reviews     Open Access   (Followers: 3)
Current Drug Metabolism     Hybrid Journal   (Followers: 3)
Current Pharmacogenomics and Personalized Medicine     Hybrid Journal   (Followers: 3)
Drug Resistance Updates     Hybrid Journal   (Followers: 3)
European Journal of Pharmacology     Hybrid Journal   (Followers: 3)
Frontiers in Medicinal Chemistry     Hybrid Journal   (Followers: 3)
Human Psychopharmacology Clinical and Experimental     Hybrid Journal   (Followers: 3)
Inflammation Research     Hybrid Journal   (Followers: 3)
Investigational New Drugs     Hybrid Journal   (Followers: 3)
Journal of Aerosol Medicine and Pulmonary Drug Delivery     Hybrid Journal   (Followers: 3)
Journal of Cardiovascular Pharmacology     Hybrid Journal   (Followers: 3)
Journal of Ethnopharmacology     Hybrid Journal   (Followers: 3)
Journal of Medical Marketing     Hybrid Journal   (Followers: 3)
Journal of Pharmacological and Toxicological Methods     Hybrid Journal   (Followers: 3)
Microbial Drug Resistance     Hybrid Journal   (Followers: 3)
International Journal of Neuropsychopharmacology     Open Access   (Followers: 3)
Therapeutic Drug Monitoring     Hybrid Journal   (Followers: 3)
Drug Metabolism Reviews     Hybrid Journal   (Followers: 3)
Acta Pharmacologica Sinica     Hybrid Journal   (Followers: 2)
Behavioural Pharmacology     Hybrid Journal   (Followers: 2)
Biomedicine & Pharmacotherapy     Full-text available via subscription   (Followers: 2)
Clinical Neuropharmacology     Hybrid Journal   (Followers: 2)
Current Drug Therapy     Hybrid Journal   (Followers: 2)
Current Enzyme Inhibition     Hybrid Journal   (Followers: 2)
Drugs in R & D     Full-text available via subscription   (Followers: 2)
Inflammopharmacology     Hybrid Journal   (Followers: 2)
Inpharma Weekly     Full-text available via subscription   (Followers: 2)
International Clinical Psychopharmacology     Hybrid Journal   (Followers: 2)
International Immunopharmacology     Hybrid Journal   (Followers: 2)
Letters in Drug Design & Discovery     Hybrid Journal   (Followers: 2)
Medicinal Research Reviews     Hybrid Journal   (Followers: 2)
Pharmacology & Therapeutics     Hybrid Journal   (Followers: 2)
Pharmacology Biochemistry and Behavior     Hybrid Journal   (Followers: 2)
Pharmacopsychiatry     Hybrid Journal   (Followers: 2)
Pulmonary Pharmacology & Therapeutics     Hybrid Journal   (Followers: 2)
Research in Social and Administrative Pharmacy     Hybrid Journal   (Followers: 2)
The Brown University Psychopharmacology Update     Hybrid Journal   (Followers: 2)
Toxicological & Environmental Chemistry     Hybrid Journal   (Followers: 2)
Toxicon     Hybrid Journal   (Followers: 2)
Journal of Microencapsulation: Microcapsules, Liposomes, Nanoparticles, Microcells, Microspheres     Hybrid Journal   (Followers: 2)
Canadian Journal of Physiology and Pharmacology     Hybrid Journal   (Followers: 1)
Current Neuropharmacology     Hybrid Journal   (Followers: 1)
Current Pharmaceutical Analysis     Hybrid Journal   (Followers: 1)
Current Vascular Pharmacology     Hybrid Journal   (Followers: 1)
Fundamental & Clinical Pharmacology     Hybrid Journal   (Followers: 1)
Journal of Drug Targeting     Hybrid Journal   (Followers: 1)
Journal of Inflammation     Open Access   (Followers: 1)
Journal of Neuroimmune Pharmacology     Hybrid Journal   (Followers: 1)
Journal of Texture Studies     Hybrid Journal   (Followers: 1)
Pharmacogenetics and Genomics     Hybrid Journal   (Followers: 1)
Particulate Science and Technology: An International Journal     Hybrid Journal   (Followers: 1)
Pharmaceutical Biology     Open Access  
Journal of Liposome Research     Hybrid Journal  
Vascular Pharmacology     Hybrid Journal  
Toxin Reviews     Hybrid Journal  
Kaohsiung Journal of Medical Sciences     Open Access  
Redox Report     Open Access  
Pharmacological Research     Hybrid Journal  
PharmacoEconomics & Outcomes News     Full-text available via subscription  
Pharmaceutical Chemistry Journal     Hybrid Journal  
NeuroMolecular Medicine     Hybrid Journal  
Journal of Ocular Pharmacology and Therapeutics     Hybrid Journal  
Harm Reduction Journal     Open Access  
Current Nanoscience     Hybrid Journal  
Infectious Disorders - Drug Targets     Hybrid Journal  
Current Bioactive Compounds     Hybrid Journal  
Cancer Biotherapy & Radiopharmaceuticals     Hybrid Journal  
Autonomic & Autacoid Pharmacology     Hybrid Journal  

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Similar Journals
Journal Cover
Clinical Trials
Journal Prestige (SJR): 2.399
Citation Impact (citeScore): 2
Number of Followers: 12  
 
  Hybrid Journal Hybrid journal (It can contain Open Access articles)
ISSN (Print) 1740-7745 - ISSN (Online) 1740-7753
Published by Sage Publications Homepage  [1176 journals]
  • The ethical value of consulting community members in non-emergency trials
           conducted with waivers of informed consent for research

    • Free pre-print version: Loading...

      Authors: Emily A Largent, Steven Joffe, Neal W Dickert, Stephanie R Morain
      Abstract: Clinical Trials, Ahead of Print.
      There is growing interest in using embedded research methods, particularly pragmatic clinical trials, to address well-known evidentiary shortcomings afflicting the health care system. Reviews of pragmatic clinical trials published between 2014 and 2019 found that 8.8% were conducted with waivers of informed consent; furthermore, the number of trials where consent is not obtained is increasing with time. From a regulatory perspective, waivers of informed consent are permissible when certain conditions are met, including that the study involves no more than minimal risk, that it could not practicably be carried out without a waiver, and that waiving consent does not violate participants’ rights and welfare. Nevertheless, when research is conducted with a waiver of consent, several ethical challenges arise. We must consider how to: address empirical evidence showing that patients and members of the public generally prefer prospective consent, demonstrate respect for persons using tools other than consent, promote public trust and investigator integrity, and ensure an adequate level of participant protections. In this article, we use examples drawn from real pragmatic clinical trials to argue that prospective consultation with representatives of the target study population can address, or at least mitigate, many of the ethical challenges posed by waivers of informed consent. We also consider what consultation might involve to illustrate its feasibility and address potential objections.
      Citation: Clinical Trials
      PubDate: 2024-06-25T09:09:33Z
      DOI: 10.1177/17407745241259360
       
  • Comparison of outcomes of the 50-year follow-up of a randomized trial
           assessed by study questionnaire and by data linkage: The CONCUR study

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      Authors: Mohammad Shahbaz, Jane E Harding, Barry Milne, Anthony Walters, Lisa Underwood, Martin von Randow, Lois Xu, Greg D Gamble
      Abstract: Clinical Trials, Ahead of Print.
      Background/Aims:Self-reported questionnaires on health status after randomized trials can be time-consuming, costly, and potentially unreliable. Administrative data sets may provide cost-effective, less biased information, but it is uncertain how administrative and self-reported data compare to identify chronic conditions in a New Zealand cohort. This study aimed to determine whether record linkage could replace self-reported questionnaires to identify chronic conditions that were the outcomes of interest for trial follow-up.Methods:Participants in 50-year follow-up of a randomized trial were asked to complete a questionnaire and to consent to accessing administrative data. The proportion of participants with diabetes, pre-diabetes, hyperlipidaemia, hypertension, mental health disorders, and asthma was calculated using each data source and agreement between data sources assessed.Results:Participants were aged 49 years (SD = 1, n = 424, 50% male). Agreement between questionnaire and administrative data was slight for pre-diabetes (kappa = 0.10), fair for hyperlipidaemia (kappa = 0.27), substantial for diabetes (kappa = 0.65), and moderate for other conditions (all kappa>0.42). Administrative data alone identified two to three times more cases than the questionnaire for all outcomes except hypertension and mental health disorders, where the questionnaire alone identified one to two times more cases than administrative data. Combining all sources increased case detection for all outcomes.Conclusions:A combination of questionnaire, pharmaceutical, and laboratory data with expert panel review were required to identify participants with chronic conditions of interest in this follow-up of a clinical trial.
      Citation: Clinical Trials
      PubDate: 2024-06-22T08:47:49Z
      DOI: 10.1177/17407745241259088
       
  • Optimizing accrual to a large-scale, clinically integrated randomized
           trial in anesthesiology: A 2-year analysis of recruitment

    • Free pre-print version: Loading...

      Authors: Hanae K Tokita, Melissa Assel, Joanna Serafin, Emily Lin, Leslie Sarraf, Geema Masson, Tracy-Ann Moo, Jonas A Nelson, Brett A Simon, Andrew J Vickers
      Abstract: Clinical Trials, Ahead of Print.
      Background:Performing large randomized trials in anesthesiology is often challenging and costly. The clinically integrated randomized trial is characterized by simplified logistics embedded into routine clinical practice, enabling ease and efficiency of recruitment, offering an opportunity for clinicians to conduct large, high-quality randomized trials under low cost. Our aims were to (1) demonstrate the feasibility of the clinically integrated trial design in a high-volume anesthesiology practice and (2) assess whether trial quality improvement interventions led to more balanced accrual among study arms and improved trial compliance over time.Methods:This is an interim analysis of recruitment to a cluster-randomized trial investigating three nerve block approaches for mastectomy with immediate implant-based reconstruction: paravertebral block (arm 1), paravertebral plus interpectoral plane blocks (arm 2), and serratus anterior plane plus interpectoral plane blocks (arm 3). We monitored accrual and consent rates, clinician compliance with the randomized treatment, and availability of outcome data. Assessment after the initial year of implementation showed a slight imbalance in study arms suggesting areas for improvement in trial compliance. Specific improvement interventions included increasing the frequency of communication with the consenting staff and providing direct feedback to clinician investigators about their individual recruitment patterns. We assessed overall accrual rates and tested for differences in accrual, consent, and compliance rates pre- and post-improvement interventions.Results:Overall recruitment was extremely high, accruing close to 90% of the eligible population. In the pre-intervention period, there was evidence of bias in the proportion of patients being accrued and receiving the monthly block, with higher rates in arm 3 (90%) compared to arms 1 (81%) and 2 (79%, p = 0.021). In contrast, in the post-intervention period, there was no statistically significant difference between groups (p = 0.8). Eligible for randomization rate increased from 89% in the pre-intervention period to 95% in the post-intervention period (difference 5.7%; 95% confidence interval = 2.2%–9.4%, p = 0.002). Consent rate increased from 95% to 98% (difference of 3.7%; 95% confidence interval = 1.1%–6.3%; p = 0.004). Compliance with the randomized nerve block approach was maintained at close to 100% and availability of primary outcome data was 100%.Conclusion:The clinically integrated randomized trial design enables rapid trial accrual with a high participant compliance rate in a high-volume anesthesiology practice. Continuous monitoring of accrual, consent, and compliance rates is necessary to maintain and improve trial conduct and reduce potential biases. This trial methodology serves as a template for the implementation of other large, low-cost randomized trials in anesthesiology.
      Citation: Clinical Trials
      PubDate: 2024-06-19T12:01:55Z
      DOI: 10.1177/17407745241255087
       
  • A survey on UK researchers’ views regarding their experiences with the
           de-identification, anonymisation, release methods and re-identification
           risk estimation for clinical trial datasets

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      Authors: Aryelly Rodriguez, Steff C Lewis, Sandra Eldridge, Tracy Jackson, Christopher J Weir
      Abstract: Clinical Trials, Ahead of Print.
      Background:There are increasing pressures for anonymised datasets from clinical trials to be shared across the scientific community. However, there is no standardised set of recommendations on how to anonymise and prepare clinical trial datasets for sharing, while an ever-increasing number of anonymised datasets are becoming available for secondary research. Our aim was to explore the current views and experiences of researchers in the United Kingdom about de-identification, anonymisation, release methods and re-identification risk estimation for clinical trial datasets.Methods:We used an online exploratory cross-sectional descriptive survey that consisted of both open-ended and closed questions.Results:We had 38 responses to invitation from June 2022 to October 2022. However, 35 participants (92%) used internal documentation and published guidance to de-identify/anonymise clinical trial datasets. De-identification, followed by anonymisation and then fulfilling data holders’ requirements before access was granted (controlled access), was the most common process for releasing the datasets as reported by 18 (47%) participants. However, 11 participants (29%) had previous knowledge of re-identification risk estimation, but they did not use any of the methodologies. Experiences in the process of de-identifying/anonymising the datasets and maintaining such datasets were mostly negative, and the main reported issues were lack of resources, guidance, and training.Conclusion:The majority of responders reported using documented processes for de-identification and anonymisation. However, our survey results clearly indicate that there are still gaps in the areas of guidance, resources and training to fulfil sharing requests of de-identified/anonymised datasets, and that re-identification risk estimation is an underdeveloped area.
      Citation: Clinical Trials
      PubDate: 2024-06-19T01:24:26Z
      DOI: 10.1177/17407745241259086
       
  • Scaling and interpreting treatment effects in clinical trials using
           restricted mean survival time

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      Authors: Theodore Karrison, Chen Hu, James Dignam
      Abstract: Clinical Trials, Ahead of Print.
      Background:Restricted mean survival time is the expected duration of survival up to a chosen time of restriction [math]. For comparison studies, the difference in restricted mean survival times between two groups provides a summary measure of the treatment effect that is free of assumptions regarding the relative shape of the two survival curves, such as proportional hazards. However, it can be difficult to judge the magnitude of the effect from a comparison of restricted means due to the truncation of observation at time [math].Methods:In this article, we describe additional ways of expressing the treatment effect based on restricted means that can be helpful in this regard. These include the ratio of restricted means, the ratio of life-years (or time) lost, and the average integrated difference between the survival curves, equal to the difference in restricted means divided by [math] These alternative metrics are straightforward to calculate and provide a means for scaling the effect size as an aid to interpretation. Examples from two randomized, multicenter clinical trials in prostate cancer, NRG/RTOG 0521 and NRG/RTOG 0534, with primary endpoints of overall survival and biochemical/radiological progression-free survival, respectively, are presented to illustrate the ideas.Results:The four effect measures (restricted mean survival time difference, restricted mean survival time ratio, time lost ratio, and average survival rate difference) were 0.45 years, 1.05, 0.81, and 0.038 for RTOG 0521 and 1.36 years, 1.17, 0.56, and 0.12 for RTOG 0534 with [math] = 12 and 11 years, respectively. Thus, for example, the 0.45-year difference in the first trial translates into a 19% reduction in time lost and a 3.8% average absolute difference between the survival curves over the 12-year horizon, a modest effect size, whereas the 1.36-year difference in the second trial corresponds to a 44% reduction in time lost and a 12% absolute survival difference, a rather large effect.Conclusions:In addition to the difference in restricted mean survival times, these alternative measures can be helpful in determining whether the magnitude of the treatment effect is clinically meaningful.
      Citation: Clinical Trials
      PubDate: 2024-06-14T05:58:46Z
      DOI: 10.1177/17407745241254995
       
  • Covariate adjustment in randomized controlled trials: General concepts and
           practical considerations

    • Free pre-print version: Loading...

      Authors: Kelly Van Lancker, Frank Bretz, Oliver Dukes
      Abstract: Clinical Trials, Ahead of Print.
      There has been a growing interest in covariate adjustment in the analysis of randomized controlled trials in past years. For instance, the US Food and Drug Administration recently issued guidance that emphasizes the importance of distinguishing between conditional and marginal treatment effects. Although these effects may sometimes coincide in the context of linear models, this is not typically the case in other settings, and this distinction is often overlooked in clinical trial practice. Considering these developments, this article provides a review of when and how to use covariate adjustment to enhance precision in randomized controlled trials. We describe the differences between conditional and marginal estimands and stress the necessity of aligning statistical analysis methods with the chosen estimand. In addition, we highlight the potential misalignment of commonly used methods in estimating marginal treatment effects. We hereby advocate for the use of the standardization approach, as it can improve efficiency by leveraging the information contained in baseline covariates while remaining robust to model misspecification. Finally, we present practical considerations that have arisen in our respective consultations to further clarify the advantages and limitations of covariate adjustment.
      Citation: Clinical Trials
      PubDate: 2024-06-03T05:39:49Z
      DOI: 10.1177/17407745241251568
       
  • Commentary on van Lancker et al

    • Free pre-print version: Loading...

      Authors: Frank E Harrell
      Abstract: Clinical Trials, Ahead of Print.

      Citation: Clinical Trials
      PubDate: 2024-06-03T05:38:49Z
      DOI: 10.1177/17407745241251609
       
  • Response to Harrell’s commentary

    • Free pre-print version: Loading...

      Authors: Kelly Van Lancker, Frank Bretz, Oliver Dukes
      Abstract: Clinical Trials, Ahead of Print.

      Citation: Clinical Trials
      PubDate: 2024-06-03T05:37:49Z
      DOI: 10.1177/17407745241251851
       
  • Multiply robust estimation of principal causal effects with noncompliance
           and survival outcomes

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      Authors: Chao Cheng, Yueqi Guo, Bo Liu, Lisa Wruck, Fan Li, Fan Li
      Abstract: Clinical Trials, Ahead of Print.
      Treatment noncompliance and censoring are two common complications in clinical trials. Motivated by the ADAPTABLE pragmatic clinical trial, we develop methods for assessing treatment effects in the presence of treatment noncompliance with a right-censored survival outcome. We classify the participants into principal strata, defined by their joint potential compliance status under treatment and control. We propose a multiply robust estimator for the causal effects on the survival probability scale within each principal stratum. This estimator is consistent even if one, sometimes two, of the four working models—on the treatment assignment, the principal strata, censoring, and the outcome—is misspecified. A sensitivity analysis strategy is developed to address violations of key identification assumptions, the principal ignorability and monotonicity. We apply the proposed approach to the ADAPTABLE trial to study the causal effect of taking low- versus high-dosage aspirin on all-cause mortality and hospitalization from cardiovascular diseases.
      Citation: Clinical Trials
      PubDate: 2024-05-30T09:53:23Z
      DOI: 10.1177/17407745241251773
       
  • Efficient designs for three-sequence stepped wedge trials with continuous
           recruitment

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      Authors: Richard Hooper, Olivier Quintin, Jessica Kasza
      Abstract: Clinical Trials, Ahead of Print.
      Background/Aims:The standard approach to designing stepped wedge trials that recruit participants in a continuous stream is to divide time into periods of equal length. But the choice of design in such cases is infinitely more flexible: each cluster could cross from the control to the intervention at any point on the continuous time-scale. We consider the case of a stepped wedge design with clusters randomised to just three sequences (designs with small numbers of sequences may be preferred for their simplicity and practicality) and investigate the choice of design that minimises the variance of the treatment effect estimator under different assumptions about the intra-cluster correlation.Methods:We make some simplifying assumptions in order to calculate the variance: in particular that we recruit the same number of participants, [math], from each cluster over the course of the trial, and that participants present at regularly spaced intervals. We consider an intra-cluster correlation that decays exponentially with separation in time between the presentation of two individuals from the same cluster, from a value of [math] for two individuals who present at the same time, to a value of [math] for individuals presenting at the start and end of the trial recruitment interval. We restrict attention to three-sequence designs with centrosymmetry – the property that if we reverse time and swap the intervention and control conditions then the design looks the same. We obtain an expression for the variance of the treatment effect estimator adjusted for effects of time, using methods for generalised least squares estimation, and we evaluate this expression numerically for different designs, and for different parameter values.Results:There is a two-dimensional space of possible three-sequence, centrosymmetric stepped wedge designs with continuous recruitment. The variance of the treatment effect estimator for given [math] and [math] can be plotted as a contour map over this space. The shape of this variance surface depends on [math] and on the parameter [math], but typically indicates a broad, flat region of close-to-optimal designs. The ‘standard’ design with equally spaced periods and 1:1:1 allocation rarely performs well, however.Conclusions:In many different settings, a relatively simple design can be found (e.g. one based on simple fractions) that offers close-to-optimal efficiency in that setting. There may also be designs that are robustly efficient over a wide range of settings. Contour maps of the kind we illustrate can help guide this choice. If efficiency is offered as one of the justifications for using a stepped wedge design, then it is worth designing with optimal efficiency in mind.
      Citation: Clinical Trials
      PubDate: 2024-05-22T06:52:38Z
      DOI: 10.1177/17407745241251780
       
  • A comparison of alternative ranking methods in two-stage clinical trials
           with multiple interventions: An application to the anxiolysis for
           laceration repair in children trial

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      Authors: Nam-Anh Tran, Abigail McGrory, Naveen Poonai, Anna Heath
      Abstract: Clinical Trials, Ahead of Print.
      Background/Aims:Multi-arm, multi-stage trials frequently include a standard care to which all interventions are compared. This may increase costs and hinders comparisons among the experimental arms. Furthermore, the standard care may not be evident, particularly when there is a large variation in standard practice. Thus, we aimed to develop an adaptive clinical trial that drops ineffective interventions following an interim analysis before selecting the best intervention at the final stage without requiring a standard care.Methods:We used Bayesian methods to develop a multi-arm, two-stage adaptive trial and evaluated two different methods for ranking interventions, the probability that each intervention was optimal (Pbest) and using the surface under the cumulative ranking curve (SUCRA), at both the interim and final analysis. The proposed trial design determines the maximum sample size for each intervention using the Average Length Criteria. The interim analysis takes place at approximately half the pre-specified maximum sample size and aims to drop interventions for futility if either Pbest or the SUCRA is below a pre-specified threshold. The final analysis compares all remaining interventions at the maximum sample size to conclude superiority based on either Pbest or the SUCRA. The two ranking methods were compared across 12 scenarios that vary the number of interventions and the assumed differences between the interventions. The thresholds for futility and superiority were chosen to control type 1 error, and then the predictive power and expected sample size were evaluated across scenarios. A trial comparing three interventions that aim to reduce anxiety for children undergoing a laceration repair in the emergency department was then designed, known as the Anxiolysis for Laceration Repair in Children Trial (ALICE) trial.Results:As the number of interventions increases, the SUCRA results in a higher predictive power compared with Pbest. Using Pbest results in a lower expected sample size when there is an effective intervention. Using the Average Length Criterion, the ALICE trial has a maximum sample size for each arm of 100 patients. This sample size results in a 86% and 85% predictive power using Pbest and the SUCRA, respectively. Thus, we chose Pbest as the ranking method for the ALICE trial.Conclusion:Bayesian ranking methods can be used in multi-arm, multi-stage trials with no clear control intervention. When more interventions are included, the SUCRA results in a higher power than Pbest. Future work should consider whether other ranking methods may also be relevant for clinical trial design.
      Citation: Clinical Trials
      PubDate: 2024-05-21T12:49:34Z
      DOI: 10.1177/17407745241251812
       
  • Comparison of Bayesian and frequentist monitoring boundaries motivated by
           the Multiplatform Randomized Clinical Trial

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      Authors: Jungnam Joo, Eric S Leifer, Michael A Proschan, James F Troendle, Harmony R Reynolds, Erinn A Hade, Patrick R Lawler, Dong-Yun Kim, Nancy L Geller
      Abstract: Clinical Trials, Ahead of Print.
      BackgroundThe coronavirus disease 2019 pandemic highlighted the need to conduct efficient randomized clinical trials with interim monitoring guidelines for efficacy and futility. Several randomized coronavirus disease 2019 trials, including the Multiplatform Randomized Clinical Trial (mpRCT), used Bayesian guidelines with the belief that they would lead to quicker efficacy or futility decisions than traditional “frequentist” guidelines, such as spending functions and conditional power. We explore this belief using an intuitive interpretation of Bayesian methods as translating prior opinion about the treatment effect into imaginary prior data. These imaginary observations are then combined with actual observations from the trial to make conclusions. Using this approach, we show that the Bayesian efficacy boundary used in mpRCT is actually quite similar to the frequentist Pocock boundary.MethodsThe mpRCT’s efficacy monitoring guideline considered stopping if, given the observed data, there was greater than 99% probability that the treatment was effective (odds ratio greater than 1). The mpRCT’s futility monitoring guideline considered stopping if, given the observed data, there was greater than 95% probability that the treatment was less than 20% effective (odds ratio less than 1.2). The mpRCT used a normal prior distribution that can be thought of as supplementing the actual patients’ data with imaginary patients’ data. We explore the effects of varying probability thresholds and the prior-to-actual patient ratio in the mpRCT and compare the resulting Bayesian efficacy monitoring guidelines to the well-known frequentist Pocock and O’Brien–Fleming efficacy guidelines. We also contrast Bayesian futility guidelines with a more traditional 20% conditional power futility guideline.ResultsA Bayesian efficacy and futility monitoring boundary using a neutral, weakly informative prior distribution and a fixed probability threshold at all interim analyses is more aggressive than the commonly used O’Brien–Fleming efficacy boundary coupled with a 20% conditional power threshold for futility. The trade-off is that more aggressive boundaries tend to stop trials earlier, but incur a loss of power. Interestingly, the Bayesian efficacy boundary with 99% probability threshold is very similar to the classic Pocock efficacy boundary.ConclusionsIn a pandemic where quickly weeding out ineffective treatments and identifying effective treatments is paramount, aggressive monitoring may be preferred to conservative approaches, such as the O’Brien–Fleming boundary. This can be accomplished with either Bayesian or frequentist methods.
      Citation: Clinical Trials
      PubDate: 2024-05-18T06:00:25Z
      DOI: 10.1177/17407745241244801
       
  • A comparison of computational algorithms for the Bayesian analysis of
           clinical trials

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      Authors: Ziming Chen, Jeffrey S Berger, Lana A Castellucci, Michael Farkouh, Ewan C Goligher, Erinn M Hade, Beverley J Hunt, Lucy Z Kornblith, Patrick R Laweler, Eric S Leifer, Elizabeth Lorenzi, Matthew D Neal, Ryan Zarychanski, Anna Heath
      Abstract: Clinical Trials, Ahead of Print.
      Background:Clinical trials are increasingly using Bayesian methods for their design and analysis. Inference in Bayesian trials typically uses simulation-based approaches such as Markov Chain Monte Carlo methods. Markov Chain Monte Carlo has high computational cost and can be complex to implement. The Integrated Nested Laplace Approximations algorithm provides approximate Bayesian inference without the need for computationally complex simulations, making it more efficient than Markov Chain Monte Carlo. The practical properties of Integrated Nested Laplace Approximations compared to Markov Chain Monte Carlo have not been considered for clinical trials. Using data from a published clinical trial, we aim to investigate whether Integrated Nested Laplace Approximations is a feasible and accurate alternative to Markov Chain Monte Carlo and provide practical guidance for trialists interested in Bayesian trial design.Methods:Data from an international Bayesian multi-platform adaptive trial that compared therapeutic-dose anticoagulation with heparin to usual care in non-critically ill patients hospitalized for COVID-19 were used to fit Bayesian hierarchical generalized mixed models. Integrated Nested Laplace Approximations was compared to two Markov Chain Monte Carlo algorithms, implemented in the software JAGS and stan, using packages available in the statistical software R. Seven outcomes were analysed: organ-support free days (an ordinal outcome), five binary outcomes related to survival and length of hospital stay, and a time-to-event outcome. The posterior distributions for the treatment and sex effects and the variances for the hierarchical effects of age, site and time period were obtained. We summarized these posteriors by calculating the mean, standard deviations and the 95% equitailed credible intervals and presenting the results graphically. The computation time for each algorithm was recorded.Results:The average overlap of the 95% credible interval for the treatment and sex effects estimated using Integrated Nested Laplace Approximations was 96% and 97.6% compared with stan, respectively. The graphical posterior densities for these effects overlapped for all three algorithms. The posterior mean for the variance of the hierarchical effects of age, site and time estimated using Integrated Nested Laplace Approximations are within the 95% credible interval estimated using Markov Chain Monte Carlo but the average overlap of the credible interval is lower, 77%, 85.6% and 91.3%, respectively, for Integrated Nested Laplace Approximations compared to stan. Integrated Nested Laplace Approximations and stan were easily implemented in clear, well-established packages in R, while JAGS required the direct specification of the model. Integrated Nested Laplace Approximations was between 85 and 269 times faster than stan and 26 and 1852 times faster than JAGS.Conclusion:Integrated Nested Laplace Approximations could reduce the computational complexity of Bayesian analysis in clinical trials as it is easy to implement in R, substantially faster than Markov Chain Monte Carlo methods implemented in JAGS and stan, and provides near identical approximations to the posterior distributions for the treatment effect. Integrated Nested Laplace Approximations was less accurate when estimating the posterior distribution for the variance of hierarchical effects, particularly for the proportional odds model, and future work should determine if the Integrated Nested Laplace Approximations algorithm can be adjusted to improve this estimation.
      Citation: Clinical Trials
      PubDate: 2024-05-16T10:06:41Z
      DOI: 10.1177/17407745241247334
       
  • Causal interpretation of the hazard ratio in randomized clinical trials

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      Authors: Michael P Fay, Fan Li
      Abstract: Clinical Trials, Ahead of Print.
      Background:Although the hazard ratio has no straightforward causal interpretation, clinical trialists commonly use it as a measure of treatment effect.Methods:We review the definition and examples of causal estimands. We discuss the causal interpretation of the hazard ratio from a two-arm randomized clinical trial, and the implications of proportional hazards assumptions in the context of potential outcomes. We illustrate the application of these concepts in a synthetic model and in a model of the time-varying effects of COVID-19 vaccination.Results:We define causal estimands as having either an individual-level or population-level interpretation. Difference-in-expectation estimands are both individual-level and population-level estimands, whereas without strong untestable assumptions the causal rate ratio and hazard ratio have only population-level interpretations. We caution users against making an incorrect individual-level interpretation, emphasizing that in general a hazard ratio does not on average change each individual’s hazard by a factor. We discuss a potentially valid interpretation of the constant hazard ratio as a population-level causal effect under the proportional hazards assumption.Conclusion:We conclude that the population-level hazard ratio remains a useful estimand, but one must interpret it with appropriate attention to the underlying causal model. This is especially important for interpreting hazard ratios over time.
      Citation: Clinical Trials
      PubDate: 2024-04-29T06:04:36Z
      DOI: 10.1177/17407745241243308
       
  • Reply to Heitjan’s commentary

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      Authors: Michael P Fay, Fan Li
      Abstract: Clinical Trials, Ahead of Print.

      Citation: Clinical Trials
      PubDate: 2024-04-29T06:03:56Z
      DOI: 10.1177/17407745241243311
       
  • Comment on “Causal interpretation of the hazard ratio in randomized
           clinical trials” by Fay and Li

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      Authors: Daniel F Heitjan
      Abstract: Clinical Trials, Ahead of Print.

      Citation: Clinical Trials
      PubDate: 2024-04-29T06:03:36Z
      DOI: 10.1177/17407745241243307
       
  • Design and implementation of community consultation for research conducted
           under exception from informed consent regulations for the PreVent and the
           PreVent 2 trials: Changes over time and during the COVID-19 pandemic

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      Authors: Tom Gugel, Karen Adams, Madelon Baranoski, N David Yanez, Michael Kampp, Tesheia Johnson, Ani Aydin, Elaine C Fajardo, Emily Sharp, Aartee Potnis, Chanel Johnson, Miriam M Treggiari
      Abstract: Clinical Trials, Ahead of Print.
      Introduction:Emergency clinical research has played an important role in improving outcomes for acutely ill patients. This is due in part to regulatory measures that allow Exception From Informed Consent (EFIC) trials. The Food and Drug Administration (FDA) requires sponsor-investigators to engage in community consultation and public disclosure activities prior to initiating an Exception From Informed Consent trial. Various approaches to community consultation and public disclosure have been described and adapted to local contexts and Institutional Review Board (IRB) interpretations. The COVID-19 pandemic has precluded the ability to engage local communities through direct, in-person public venues, requiring research teams to find alternative ways to inform communities about emergency research.Methods:The PreVent and PreVent 2 studies were two Exception From Informed Consent trials of emergency endotracheal intubation, conducted in one geographic location for the PreVent Study and in two geographic locations for the PreVent 2 Study. During the period of the two studies, there was a substantial shift in the methodological approach spanning across the periods before and after the pandemic from telephone, to in-person, to virtual settings.Results:During the 10 years of implementation of Exception From Informed Consent activities for the two PreVent trials, there was overall favorable public support for the concept of Exception From Informed Consent trials and for the importance of emergency clinical research. Community concerns were few and also did not differ much by method of contact. Attendance was higher with the implementation of virtual technology to reach members of the community, and overall feedback was more positive compared with telephone contacts or in-person events. However, the proportion of survey responses received after completion of the remote, live event was substantially lower, with a greater proportion of respondents having higher education levels. This suggests less active engagement after completion of the synchronous activity and potentially higher selection bias among respondents. Importantly, we found that engagement with local community leaders was a key component to develop appropriate plans to connect with the public.Conclusion:The PreVent experience illustrated operational advantages and disadvantages to community consultation conducted primarily by telephone, in-person events, or online activities. Approaches to enhance community acceptance included partnering with community leaders to optimize the communication strategies and trust building with the involvement of Institutional Review Board representatives during community meetings. Researchers might need to pivot from in-person planning to virtual techniques while maintaining the ability to engage with the public with two-way communication approaches. Due to less active engagement, and potential for selection bias in the responders, further research is needed to address the costs and benefits of virtual community consultation and public disclosure activities compared to in-person events.
      Citation: Clinical Trials
      PubDate: 2024-04-27T09:25:15Z
      DOI: 10.1177/17407745241243045
       
  • Critical importance of correctly defining and reporting secondary
           endpoints when assessing the ethics of research biopsies

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      Authors: Laura A Levit, Elizabeth Garrett-Mayer, Jeffrey Peppercorn, Mark J Ratain
      Abstract: Clinical Trials, Ahead of Print.
      This article reviews the implementation challenges to the American Society of Clinical Oncology’s ethical framework for including research biopsies in oncology clinical trials. The primary challenges to implementation relate to the definitions of secondary endpoints, the scientific and regulatory framework, and the incentive structure that encourages inclusion of biopsies. Principles of research stewardship require that the clinical trials community correctly articulate the scientific goals of any research biopsies, especially those that are required for the patient to enroll on a trial and receive an investigational agent. Furthermore, it is important to sufficiently justify the characterization of secondary (as distinguished from exploratory) endpoints, protect the interest of research participants, and report accurate and complete information to ClinicalTrials.gov and the published literature.
      Citation: Clinical Trials
      PubDate: 2024-04-24T03:56:39Z
      DOI: 10.1177/17407745241244753
       
  • Reconsidering stepped wedge cluster randomized trial designs with
           implementation periods: Fewer sequences or the parallel-group design with
           baseline and implementation periods are potentially more efficient

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      Authors: Philip M Westgate, Shawn R Nigam, Abigail B Shoben
      Abstract: Clinical Trials, Ahead of Print.
      Background/aimsWhen designing a cluster randomized trial, advantages and disadvantages of tentative designs must be weighed. The stepped wedge design is popular for multiple reasons, including its potential to increase power via improved efficiency relative to a parallel-group design. In many realistic settings, it will take time for clusters to fully implement the intervention. When designing the HEALing (Helping to End Addiction Long-termSM) Communities Study, implementation time was a major consideration, and we examined the efficiency and practicality of three designs. Specifically, a three-sequence stepped wedge design with implementation periods, a corresponding two-sequence modified design that is created by removing the middle sequence, and a parallel-group design with baseline and implementation periods. In this article, we study the relative efficiencies of these specific designs. More generally, we study the relative efficiencies of modified designs when the stepped wedge design with implementation periods has three or more sequences. We also consider different correlation structures.MethodsWe compare efficiencies of stepped wedge designs with implementation periods consisting of three to nine sequences with a variety of corresponding designs. The three-sequence design is compared to the two-sequence modified design and to the parallel-group design with baseline and implementation periods analysed via analysis of covariance. Stepped wedge designs with implementation periods consisting of four or more sequences are compared to modified designs that remove all or a subset of ‘middle’ sequences. Efficiencies are based on the use of linear mixed effects models.ResultsIn the studied settings, the modified design is more efficient than the three-sequence stepped wedge design with implementation periods. The parallel-group design with baseline and implementation periods with analysis of covariance–based analysis is often more efficient than the three-sequence design. With respect to stepped wedge designs with implementation periods that are comprised of more sequences, there are often corresponding modified designs that improve efficiency. However, use of only the first and last sequences has the potential to be either relatively efficient or inefficient. Relative efficiency is impacted by the strength of the statistical correlation among outcomes from the same cluster; for example, the relative efficiencies of modified designs tend to be greater for smaller cluster auto-correlation values.ConclusionIf a three-sequence stepped wedge design with implementation periods is being considered for a future cluster randomized trial, then a corresponding modified design using only the first and last sequences should be considered if sole focus is on efficiency. However, a parallel-group design with baseline and implementation periods and analysis of covariance–based analysis can be a practical, efficient alternative. For stepped wedge designs with implementation periods and a larger number of sequences, modified versions that remove ‘middle’ sequences should be considered. Due to the potential sensitivity of design efficiencies, statistical correlation should be carefully considered.
      Citation: Clinical Trials
      PubDate: 2024-04-23T05:26:44Z
      DOI: 10.1177/17407745241244790
       
  • The 3 + 3 design in dose-finding studies with small sample sizes:
           Pitfalls and possible remedies

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      Authors: Cody Chiuzan, Hakim-Moulay Dehbi
      Abstract: Clinical Trials, Ahead of Print.
      In the last few years, numerous novel designs have been proposed to improve the efficiency and accuracy of phase I trials to identify the maximum-tolerated dose (MTD) or the optimal biological dose (OBD) for noncytotoxic agents. However, the conventional 3+3 approach, known for its and poor performance, continues to be an attractive choice for many trials despite these alternative suggestions. The article seeks to underscore the importance of moving beyond the 3+3 design by highlighting a different key element in trial design: the estimation of sample size and its crucial role in predicting toxicity and determining the MTD. We use simulation studies to compare the performance of the most used phase I approaches: 3+3, Continual Reassessment Method (CRM), Keyboard and Bayesian Optimal Interval (BOIN) designs regarding three key operating characteristics: the percentage of correct selection of the true MTD, the average number of patients allocated per dose level, and the average total sample size. The simulation results consistently show that the 3+3 algorithm underperforms in comparison to model-based and model-assisted designs across all scenarios and metrics. The 3+3 method yields significantly lower (up to three times) probabilities in identifying the correct MTD, often selecting doses one or even two levels below the actual MTD. The 3+3 design allocates significantly fewer patients at the true MTD, assigns higher numbers to lower dose levels, and rarely explores doses above the target dose-limiting toxicity (DLT) rate. The overall performance of the 3+3 method is suboptimal, with a high level of unexplained uncertainty and significant implications for accurately determining the MTD. While the primary focus of the article is to demonstrate the limitations of the 3+3 algorithm, the question remains about the preferred alternative approach. The intention is not to definitively recommend one model-based or model-assisted method over others, as their performance can vary based on parameters and model specifications. However, the presented results indicate that the CRM, Keyboard, and BOIN designs consistently outperform the 3+3 and offer improved efficiency and precision in determining the MTD, which is crucial in early-phase clinical trials.
      Citation: Clinical Trials
      PubDate: 2024-04-15T09:50:05Z
      DOI: 10.1177/17407745241240401
       
  • Evaluating treatment efficacy in hospitalized COVID-19 patients, with
           applications to Adaptive COVID-19 Treatment Trials

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      Authors: Dan-Yu Lin, Jianqiao Wang, Yu Gu, Donglin Zeng
      Abstract: Clinical Trials, Ahead of Print.
      BackgroundThe current endpoints for therapeutic trials of hospitalized COVID-19 patients capture only part of the clinical course of a patient and have limited statistical power and robustness.MethodsWe specify proportional odds models for repeated measures of clinical status, with a common odds ratio of lower severity over time. We also specify the proportional hazards model for time to each level of improvement or deterioration of clinical status, with a common hazard ratio for overall treatment benefit. We apply these methods to Adaptive COVID-19 Treatment Trials.ResultsFor remdesivir versus placebo, the common odds ratio was 1.48 (95% confidence interval (CI) = 1.23–1.79; p 
      Citation: Clinical Trials
      PubDate: 2024-04-15T09:49:44Z
      DOI: 10.1177/17407745241238443
       
  • Considerations for open-label randomized clinical trials: Design, conduct,
           and analysis

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      Authors: Karen M Higgins, Gregory Levin, Robert Busch
      Abstract: Clinical Trials, Ahead of Print.
      Randomization and blinding are regarded as the most important tools to help reduce bias in clinical trial designs. Randomization is used to help guarantee that treatment arms differ systematically only by treatment assignment at baseline, and blinding is used to ensure that differences in endpoint evaluation and clinical decision-making during the trial arise only from the treatment received and not, for example, the expectation or desires of the people involved. However, given that there are times when it is not feasible or ethical to conduct fully blinded trials, we discuss what can be done to improve a trial, including conducting the trial as if it were a fully blinded trial and maintaining confidentiality of ongoing study results. In this article, we review how best to design, conduct, and analyze open-label trials to ensure the highest level of study integrity and the reliability of the study conclusions.
      Citation: Clinical Trials
      PubDate: 2024-04-15T08:45:47Z
      DOI: 10.1177/17407745241244788
       
  • Accrual Quality Improvement Program for clinical trials

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      Authors: Ellen Richmond, Goli Samimi, Margaret House, Leslie G Ford, Eva Szabo
      Abstract: Clinical Trials, Ahead of Print.
      BackgroundThe Early Phase Cancer Prevention Clinical Trials Program (Consortia), led by the Division of Cancer Prevention, National Cancer Institute, supports and conducts trials assessing safety, tolerability, and cancer preventive potential of a variety of interventions. Accrual to cancer prevention trials includes the recruitment of unaffected populations, posing unique challenges related to minimizing participant burden and risk, given the less evident or measurable benefits to individual participants. The Accrual Quality Improvement Program was developed to address these challenges and better understand the multiple determinants of accrual activity throughout the life of the trial. Through continuous monitoring of accrual data, Accrual Quality Improvement Program identifies positive and negative factors in real-time to optimize enrollment rates for ongoing and future trials.MethodsThe Accrual Quality Improvement Program provides a web-based centralized infrastructure for collecting, analyzing, visualizing, and storing qualitative and quantitative participant-, site-, and study-level data. The Accrual Quality Improvement Program approaches cancer prevention clinical trial accrual as multi-factorial, recognizing protocol design, potential participants’ characteristics, and individual site as well as study-wide implementation issues.ResultsThe Accrual Quality Improvement Program was used across 39 Consortia trials from 2014 to 2022 to collect comprehensive trial information. The Accrual Quality Improvement Program captures data at the participant level, including number of charts reviewed, potential participants contacted and reasons why participants were not eligible for contact or did not consent to the trial or start intervention. The Accrual Quality Improvement Program also captures site-level (e.g. staffing issues) and study-level (e.g. when protocol amendments are made) data at each step of the recruitment/enrollment process, from potential participant identification to contact, consent, intervention, and study completion using a Recruitment Journal. Accrual Quality Improvement Program’s functionality also includes tracking and visualization of a trial’s cumulative accrual rate compared to the projected accrual rate, including a zone-based performance rating with corresponding quality improvement intervention recommendations.ConclusionThe challenges associated with recruitment and timely completion of early phase cancer prevention clinical trials necessitate a data collection program capable of continuous collection and quality improvement. The Accrual Quality Improvement Program collects cumulative data across National Cancer Institute, Division of Cancer Prevention early phase clinical trials, providing the opportunity for real-time review of participant-, site-, and study-level data and thereby enables responsive recruitment strategy and protocol modifications for improved recruitment rates to ongoing trials. Of note, Accrual Quality Improvement Program data collected from ongoing trials will inform future trials to optimize protocol design and maximize accrual efficiency.
      Citation: Clinical Trials
      PubDate: 2024-04-09T12:19:04Z
      DOI: 10.1177/17407745241243027
       
  • Dose optimization for cancer treatments with considerations for late-onset
           toxicities

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      Authors: Lucie Biard, Anaïs Andrillon, Rebecca B Silva, Shing M Lee
      Abstract: Clinical Trials, Ahead of Print.
      Given that novel anticancer therapies have different toxicity profiles and mechanisms of action, it is important to reconsider the current approaches for dose selection. In an effort to move away from considering the maximum tolerated dose as the optimal dose, the Food and Drug Administration Project Optimus points to the need of incorporating long-term toxicity evaluation, given that many of these novel agents lead to late-onset or cumulative toxicities and there are no guidelines on how to handle them. Numerous methods have been proposed to handle late-onset toxicities in dose-finding clinical trials. A summary and comparison of these methods are provided. Moreover, using PI3K inhibitors as a case study, we show how late-onset toxicity can be integrated into the dose-optimization strategy using current available approaches. We illustrate a re-design of this trial to compare the approach to those that only consider early toxicity outcomes and disregard late-onset toxicities. We also provide proposals going forward for dose optimization in early development of novel anticancer agents with considerations for late-onset toxicities.
      Citation: Clinical Trials
      PubDate: 2024-04-09T10:52:10Z
      DOI: 10.1177/17407745231221152
       
  • The overlap between randomised evaluations of recruitment and retention
           interventions: An updated review of recruitment (Online Resource for
           Recruitment in Clinical triAls) and retention (Online Resource for
           Retention in Clinical triAls) literature

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      Authors: Anna Kearney, Laura Butlin, Taylor Coffey, Thomas Conway, Sarah Cotterill, Alison Evans, Jackie Fox, Andrew Hunter, Sarah Inglis, Louise Murphy, Nurulamin M Noor, Terrie Walker-Smith, Carrol Gamble
      Abstract: Clinical Trials, Ahead of Print.
      BackgroundThe Online Resource for Recruitment in Clinical triAls (ORRCA) and the Online Resource for Retention in Clinical triAls (ORRCA2) were established to organise and map the literature addressing participant recruitment and retention within clinical research. The two databases are updated on an ongoing basis using separate but parallel systematic reviews. However, recruitment and retention of research participants is widely acknowledged to be interconnected. While interventions aimed at addressing recruitment challenges can impact retention and vice versa, it is not clear how well they are simultaneously considered within methodological research. This study aims to report the recent update of ORRCA and ORRCA2 with a special emphasis on assessing crossover of the databases and how frequently randomised studies of methodological interventions measure the impact on both recruitment and retention outcomes.MethodsTwo parallel systematic reviews were conducted in line with previously reported methods updating ORRCA (recruitment) and ORRCA2 (retention) with publications from 2018 and 2019. Articles were categorised according to their evidence type (randomised evaluation, non-randomised evaluation, application and observation) and against the recruitment and retention domain frameworks. Articles categorised as randomised evaluations were compared to identify studies appearing in both databases. For randomised studies that were only in one database, domain categories were used to assess whether the methodological intervention was likely to impact on the alternate construct. For example, whether a recruitment intervention might also impact retention.ResultsIn total, 806 of 17,767 articles screened for the recruitment database and 175 of 18,656 articles screened for the retention database were added as result of the update. Of these, 89 articles were classified as ‘randomised evaluation’, of which 6 were systematic reviews and 83 were randomised evaluations of methodological interventions. Ten of the randomised studies assessed recruitment and retention and were included in both databases. Of the randomised studies only in the recruitment database, 48/55 (87%) assessed the content or format of participant information which could have an impact on retention. Of the randomised studies only in the retention database, 6/18 (33%) assessed monetary incentives, 4/18 (22%) assessed data collection location and methods and 3/18 (17%) assessed non-monetary incentives, all of which could have an impact on recruitment.ConclusionOnly a small proportion of randomised studies of methodological interventions assessed the impact on both recruitment and retention despite having a potential impact on both outcomes. Where possible, an integrated approach analysing both constructs should be the new standard for these types of evaluations to ensure that improvements to recruitment are not at the expense of retention and vice versa.
      Citation: Clinical Trials
      PubDate: 2024-04-05T04:14:04Z
      DOI: 10.1177/17407745241238444
       
  • Current issues in dose-finding designs: A response to the US Food and Drug
           Adminstrations’s oncology center of excellence project optimus

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      Authors: Peter F Thall, Elizabeth Garrett-Mayer, Nolan A Wages, Susan Halabi, Ying Kuen Cheung
      Abstract: Clinical Trials, Ahead of Print.
      With the advent of targeted agents and immunological therapies, the medical research community has become increasingly aware that conventional methods for determining the best dose or schedule of a new agent are inadequate. It has been well established that conventional phase I designs cannot reliably identify safe and effective doses. This problem applies, generally, for cytotoxic agents, radiation therapy, targeted agents, and immunotherapies. To address this, the US Food and Drug Administration’s Oncology Center of Excellence initiated Project Optimus, with the goal “to reform the dose optimization and dose selection paradigm in oncology drug development.” As a response to Project Optimus, the articles in this special issue of Clinical Trials review recent advances in methods for choosing the dose or schedule of a new agent with an overall objective of informing clinical trialists of these innovative designs. This introductory article briefly reviews problems with conventional methods, the regulatory changes that encourage better dose optimization designs, and provides brief summaries of the articles that follow in this special issue.
      Citation: Clinical Trials
      PubDate: 2024-04-04T04:00:55Z
      DOI: 10.1177/17407745241234652
       
  • Applications of the partial-order continual reassessment method in the
           early development of treatment combinations

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      Authors: Nolan A Wages, Patrick M Dillon, Craig A Portell, Craig L Slingluff, Gina R Petroni
      Abstract: Clinical Trials, Ahead of Print.
      Combination therapy is increasingly being explored as a promising approach for improving cancer treatment outcomes. However, identifying effective dose combinations in early oncology drug development is challenging due to limited sample sizes in early-phase clinical trials. This task becomes even more complex when multiple agents are being escalated simultaneously, potentially leading to a loss of monotonic toxicity order with respect to the dose. Traditional single-agent trial designs are insufficient for this multi-dimensional problem, necessitating the development and implementation of dose-finding methods specifically designed for drug combinations. While, in practice, approaches to this problem have focused on preselecting combinations with a known toxicity order and applying single-agent designs, this limits the number of combinations considered and may miss promising dose combinations. In recent years, several novel designs have been proposed for exploring partially ordered drug combination spaces with the goal of identifying a maximum tolerated dose combination, based on safety, or an optimal dose combination, based on toxicity and efficacy. However, their implementation in clinical practice remains limited. In this article, we describe the application of the partial order continual reassessment method and its extensions for combination therapies in early-phase clinical trials. We present completed trials that use safety endpoints to identify maximum tolerated dose combinations and adaptively use both safety and efficacy endpoints to determine optimal treatment strategies. We discuss the effectiveness of the partial-order continual reassessment method and its extensions in identifying optimal treatment strategies and provide our experience with executing these novel adaptive designs in practice. By utilizing innovative dose-finding methods, researchers and clinicians can more effectively navigate the challenges of combination therapy development, ultimately improving patient outcomes in the treatment of cancer.
      Citation: Clinical Trials
      PubDate: 2024-03-30T10:52:55Z
      DOI: 10.1177/17407745241234634
       
  • Society for Clinical Trials Data Monitoring Committee initiative website:
           Closing the gap

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      Authors: David L DeMets, Susan Halabi, Lehana Thabane, Janet Wittes
      Abstract: Clinical Trials, Ahead of Print.

      Citation: Clinical Trials
      PubDate: 2024-03-30T04:05:36Z
      DOI: 10.1177/17407745241238393
       
  • Challenges in conducting efficacy trials for new COVID-19 vaccines in
           developed countries

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      Authors: Rafael Dal-Ré, Emmanuel Bottieau, Odile Launay, Frits R. Rosendaal, Brigitte Schwarzer-Daum
      Abstract: Clinical Trials, Ahead of Print.
      The protection from COVID-19 vaccination wanes a few months post-administration of the primary vaccination series or booster doses. New COVID-19 vaccine candidates aiming to help control COVID-19 should show long-term efficacy, allowing a possible annual administration. Until correlates of protection are strongly associated with long-term protection, it has been suggested that any new COVID-19 vaccine candidate must demonstrate at least 75% efficacy (although a 40%–60% efficacy would be sufficient) at 12 months in preventing illness in all age groups within a large randomized controlled efficacy trial. This article discusses four of the many scientific, ethical, and operational challenges that these trials will face in developed countries, focusing on a pivotal trial in adults. These challenges are (1) the comparator and trial population; (2) how to enroll sufficient numbers of adult participants of all age groups considering that countries will recommend COVID-19 booster doses to different populations; (3) whether having access to a comparator booster for the trial is actually feasible; and (4) the changing epidemiology of severe acute respiratory syndrome coronavirus 2 across countries involved in the trial. It is desirable that regulatory agencies publish guidance on the requirements that a trial like the one discussed should comply with to be acceptable from a regulatory standpoint. Ideally, this should happen even before there is a vaccine candidate that could fulfill the requirements mentioned above, as it would allow an open discussion among all stakeholders on its appropriateness and feasibility.
      Citation: Clinical Trials
      PubDate: 2024-03-29T11:16:38Z
      DOI: 10.1177/17407745241238925
       
  • Assessing the current utilization status of wearable devices in clinical
           research

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      Authors: Takashi Miyakoshi, Yoichi M Ito
      Abstract: Clinical Trials, Ahead of Print.
      Background/AimsInformation regarding the use of wearable devices in clinical research, including disease areas, intervention techniques, trends in device types, and sample size targets, remains elusive. Therefore, we conducted a comprehensive review of clinical research trends related to wristband wearable devices in research planning and examined their applications in clinical investigations.MethodsAs this study identified trends in the adoption of wearable devices during the planning phase of clinical research, including specific disease areas and targeted number of intervention cases, we searched ClinicalTrials.gov—a prominent platform for registering and disseminating clinical research. Since wrist-worn devices represent a large share of the market, we focused on wrist-worn devices and selected the most representative models among them. The main analysis focused on major wearable devices to facilitate data analysis and interpretation, but other wearables were also surveyed for reference. We searched ClinicalTrials.gov with the keywords “ActiGraph,”“Apple Watch,”“Empatica,”“Fitbit,”“Garmin,” and “wearable devices” to obtain studies published up to 21 August 2022. This initial search yielded 3214 studies. After excluding duplicate National Clinical Trial studies (the overlap was permissible among different device types except for wearable devices), our analysis focused on 2930 studies, including simple, time-series, and type-specific assessments of various variables.ResultsOverall, an increasing number of clinical studies have incorporated wearable devices since 2012. While ActiGraph and Fitbit initially dominated this landscape, the use of other devices has steadily increased, constituting approximately 10% of the total after 2015. Observational studies outnumbered intervention studies, with behavioral and device-based interventions being particularly prevalent. Regarding disease types, cancer and cardiovascular diseases accounted for approximately 20% of the total. Notably, 114 studies adopted multiple devices simultaneously within the context of their clinical investigations.ConclusionsOur findings revealed that the utilization of wearable devices for data collection and behavioral interventions in various disease areas has been increasing over time since 2012. The increase in the number of studies over the past 3 years has been particularly significant, suggesting that this trend will continue to accelerate in the future. Devices and their evaluation methods that have undergone thorough validation, confirmed their accuracy, and adhered to established legal regulations will likely assume a pivotal role in evaluations, allowing for remote clinical trials. Moreover, behavioral intervention therapy utilizing apps is becoming more extensive, and we expect to see more examples that will lead to their approval as programmed medical devices in the future.
      Citation: Clinical Trials
      PubDate: 2024-03-15T05:01:14Z
      DOI: 10.1177/17407745241230287
       
  • A safety estimand for late phase clinical trials where the analysis period
           varies over the subjects

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      Authors: Katarina Hedman, Vera Lisovskaja, Per Nyström
      Abstract: Clinical Trials, Ahead of Print.
      Background/AimsEvaluating safety is as important as evaluating efficacy in a clinical trial, yet the tradition for safety analysis is rudimentary. This article explores more complex methodologies for safety evaluation, with the aim of improving the interpretability, as well as generalizability, of the results.MethodsFor studies where the analysis periods vary over the subjects, using the International Council for Harmonisation estimand framework, we construct a formal estimand that could be used in the setting of safety surveillance that answers the clinical question of ‘What is the magnitude of the increase in risk of experiencing an adverse event if the treatment is taken, as prescribed, for a specific period of time'’. Estimation methodologies for this estimand are also discussed.ResultsThe proposed estimand is similar to that found in the efficacy analyses of time to event data (e.g. in outcome studies), with the key difference of utilization of hypothetical intercurrent event strategy for the intercurrent event of treatment discontinuation. This is motivated by what we perceive to be a key difference for the safety objective compared to efficacy objectives, namely a desire for sensitivity (i.e. greater possibility of detecting a negative impact of the drug, if such exists) as opposed to the need to prove a positive effect of the drug in a conservative manner.ConclusionIt is valuable, and possible, to use the International Council for Harmonisation estimand framework not only for efficacy but also for safety evaluation, with the estimand driven by an interpretable, and relevant, clinical question.
      Citation: Clinical Trials
      PubDate: 2024-03-01T06:05:18Z
      DOI: 10.1177/17407745241230933
       
  • Assessing the impact of risk-based data monitoring on outcomes for a
           paediatric multicentre randomised controlled trial

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      Authors: Renate Le Marsney, Kerry Johnson, Jenipher Chumbes Flores, Shelley Coetzer, Jennifer Darvas, Carmel Delzoppo, Arielle Jolly, Kate Masterson, Claire Sherring, Hannah Thomson, Endrias Ergetu, Patricia Gilholm, Kristen S Gibbons
      Abstract: Clinical Trials, Ahead of Print.
      Background/AimsRegulatory guidelines recommend that sponsors develop a risk-based approach to monitoring clinical trials. However, there is a lack of evidence to guide the effective implementation of monitoring activities encompassed in this approach. The aim of this study was to assess the efficiency and impact of the risk-based monitoring approach used for a multicentre randomised controlled trial comparing treatments in paediatric patients undergoing cardiac bypass surgery.MethodsThis is a secondary analysis of data from a randomised controlled trial that implemented targeted source data verification as part of the risk-based monitoring approach. Monitoring duration and source to database error rates were calculated across the monitored trial dataset. The monitored and unmonitored trial dataset, and simulated trial datasets with differing degrees of source data verification and cohort sizes were compared for their effect on trial outcomes.ResultsIn total, 106,749 critical data points across 1,282 participants were verified from source data either remotely or on-site during the trial. The total time spent monitoring was 365 hours, with a median (interquartile range) of 10 (7, 16) minutes per participant. An overall source to database error rate of 3.1% was found, and this did not differ between treatment groups. A low rate of error was found for all outcomes undergoing 100% source data verification, with the exception of two secondary outcomes with error rates>10%. Minimal variation in trial outcomes were found between the unmonitored and monitored datasets. Reduced degrees of source data verification and reduced cohort sizes assessed using simulated trial datasets had minimal impact on trial outcomes.ConclusionsTargeted source data verification of data critical to trial outcomes, which carried with it a substantial time investment, did not have an impact on study outcomes in this trial. This evaluation of the cost-effectiveness of targeted source data verification contributes to the evidence-base regarding the context where reduced emphasis should be placed on source data verification as the foremost monitoring activity.
      Citation: Clinical Trials
      PubDate: 2024-02-29T11:20:05Z
      DOI: 10.1177/17407745231222019
       
  • Public involvement in Australian clinical trials: A systematic review

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      Authors: Tessa-May Zirnsak, Ashley H Ng, Catherine Brasier, Richard Gray
      Abstract: Clinical Trials, Ahead of Print.
      BackgroundPublic involvement enhances the relevance, quality, and impact of research. There is some evidence that public involvement in Australian research lags other countries, such as the United Kingdom. The purpose of the systematic review was to establish the rates and describe the characteristics of public involvement in Australian clinical trials.MethodsWe reviewed evidence of public involvement in all Australian randomised controlled trials published in the first 6 months of 2021. To determine the quality of public involvement, we used the five-item short-form version of the Guidance of Reporting Involvement Patients and the Public, version 2.ResultsIn total, 325 randomised controlled trials were included, of which 17 (5%) reported any public involvement. Six trials reported public involvement in setting the research aim and seven in developing study methods. The authors of one study reflected on the overall role and influence of public involvement in the research.ConclusionRate of public involvement in Australian clinical trials is seemingly substantially lower than those reported in countries with similar advanced public health care systems, notably the United Kingdom. Our observations may be explained by a lack of researcher skills in how to involve the public and the failure by major funding agencies in Australia to mandate public involvement when deciding on how to award grant funding.
      Citation: Clinical Trials
      PubDate: 2024-02-27T04:25:55Z
      DOI: 10.1177/17407745231224533
       
  • The patient perspective on dose optimization for anticancer treatments: A
           new era of cancer drug dosing—Challenging the “more is better” dogma
           

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      Authors: Julia Maués, Anne Loeser, Janice Cowden, Sheila Johnson, Martha Carlson, Shing Lee
      Abstract: Clinical Trials, Ahead of Print.
      The Patient-Centered Dosing Initiative, a patient-led effort advocating for a paradigm shift in determining cancer drug dosing strategies, pioneers a departure from traditional oncology drug dosing practices. Historically, oncology drug dosing relies on identifying the maximum tolerated dose through phase 1 dose escalation methodology, favoring higher dosing for greater efficacy, often leading to higher toxicity. However, this approach is not universally applicable, especially for newer treatments like targeted therapies and immunotherapies. Patient-Centered Dosing Initiative challenges this “more is better” ethos, particularly as metastatic breast cancer patients themselves, as they not only seek longevity but also a high quality of life since most metastatic breast cancer patients stay on treatment for the rest of their lives. Surveying 1221 metastatic breast cancer patients and 119 oncologists revealed an evident need for flexible dosing strategies, advocating personalized care discussions based on patient attributes. The survey results also demonstrated an openness toward flexible dosing and a willingness from both patients and clinicians to discuss dosing as part of their care. Patient-centered dosing emphasizes dialogue between clinicians and patients, delving into treatment efficacy-toxicity trade-offs. Similarly, clinical trial advocacy for multiple dosing regimens encourages adaptive strategies, moving away from strict adherence to maximum tolerated dose, supported by recent research in optimizing drug dosages. Recognizing the efficacy-effectiveness gap between clinical trials and real-world practice, Patient-Centered Dosing Initiative underscores the necessity for patient-centered dosing strategies. A focus on individual patient attributes aligns with initiatives like Project Optimus and Project Renewal, aiming to optimize drug dosages for improved treatment outcomes at both the pre- and post-approval phases. Patient-Centered Dosing Initiative’s efforts extend to patient education, providing tools to initiate dosage-related conversations with physicians. In addition, it emphasizes physician-patient dialogues and post-marketing studies as essential in determining optimal dosing and refining drug regimens. A dose-finding paradigm prioritizing drug safety, tolerability, and efficacy benefits all stakeholders, reducing emergency care needs and missed treatments for patients, aligning with oncologists’ and patients’ shared goals. Importantly, it represents a win-win scenario across healthcare sectors. In summary, the Patient-Centered Dosing Initiative drives transformative changes in cancer drug dosing, emphasizing patient well-being and personalized care, aiming to enhance treatment outcomes and optimize oncology drug delivery.
      Citation: Clinical Trials
      PubDate: 2024-02-22T10:45:13Z
      DOI: 10.1177/17407745241232428
       
  • Rethinking the clinical research protocol: Lessons learned from the
           COVID-19 pandemic and recommendations for reducing noncompliance

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      Authors: Matthew J Gooden, Gina Norato, Katherine Landry, Sandra B Martin, Avindra Nath, Lauren Reoma
      Abstract: Clinical Trials, Ahead of Print.
      Background/AimsSince the onset of the coronavirus disease 2019 (COVID-19) pandemic, 103.4 million cases and 1.1 million deaths have occurred nationally as of November 2023. Despite the benefit of mitigating measures, the pandemic’s effect on participant safety is rarely documented.MethodsThis study assessed noncompliance occurring from July 2019 to August 2021 that were stratified by the date of noncompliance (before or after restrictions). Events were described by size, site, noncompliance type, primary category, subcategory, and cause. In addition, noncompliance associated with COVID-19 was analyzed to determine characteristics.ResultsIn total, 323 noncompliance events occurred across 21,146 participants at risk in 35 protocols. The overall rate of noncompliance increased from 0.008 events per participant to 0.022 events per participant after the COVID-19 restrictions (p 
      Citation: Clinical Trials
      PubDate: 2024-02-17T10:05:47Z
      DOI: 10.1177/17407745241232430
       
  • The use of linked administrative data in Australian randomised controlled
           trials: A scoping review

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      Authors: Salma Fahridin, Neeru Agarwal, Karen Bracken, Stephen Law, Rachael L Morton
      Abstract: Clinical Trials, Ahead of Print.
      Background/Aims:The demand for simplified data collection within trials to increase efficiency and reduce costs has led to broader interest in repurposing routinely collected administrative data for use in clinical trials research. The aim of this scoping review is to describe how and why administrative data have been used in Australian randomised controlled trial conduct and analyses, specifically the advantages and limitations of their use as well as barriers and enablers to accessing administrative data for use alongside randomised controlled trials.Methods:Databases were searched to November 2022. Randomised controlled trials were included if they accessed one or more Australian administrative data sets, where some or all trial participants were enrolled in Australia, and where the article was published between January 2000 and November 2022. Titles and abstracts were independently screened by two reviewers, and the full texts of selected studies were assessed against the eligibility criteria by two independent reviewers. Data were extracted from included articles by two reviewers using a data extraction tool.Results:Forty-one articles from 36 randomised controlled trials were included. Trial characteristics, including the sample size, disease area, population, and intervention, were varied; however, randomised controlled trials most commonly linked to government reimbursed claims data sets, hospital admissions data sets and birth/death registries, and the most common reason for linkage was to ascertain disease outcomes or survival status, and to track health service use. The majority of randomised controlled trials were able to achieve linkage in over 90% of trial participants; however, consent and participant withdrawals were common limitations to participant linkage. Reported advantages were the reliability and accuracy of the data, the ease of long term follow-up, and the use of established data linkage units. Common reported limitations were locating participants who had moved outside the jurisdictional area, missing data where consent was not provided, and unavailability of certain healthcare data.Conclusions:As linked administrative data are not intended for research purposes, detailed knowledge of the data sets is required by researchers, and the time delay in receiving the data is viewed as a barrier to its use. The lack of access to primary care data sets is viewed as a barrier to administrative data use; however, work to expand the number of healthcare data sets that can be linked has made it easier for researchers to access and use these data, which may have implications on how randomised controlled trials will be run in future.
      Citation: Clinical Trials
      PubDate: 2024-02-02T12:06:10Z
      DOI: 10.1177/17407745231225618
       
  • Is inadequate risk stratification diluting hazard ratio estimates in
           randomized clinical trials'

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      Authors: Devan V Mehrotra, Rachel Marceau West
      Abstract: Clinical Trials, Ahead of Print.
      In randomized clinical trials, analyses of time-to-event data without risk stratification, or with stratification based on pre-selected factors revealed at the end of the trial to be at most weakly associated with risk, are quite common. We caution that such analyses are likely delivering hazard ratio estimates that unwittingly dilute the evidence of benefit for the test relative to the control treatment. To make our case, first, we use a hypothetical scenario to contrast risk-unstratified and risk-stratified hazard ratios. Thereafter, we draw attention to the previously published 5-step stratified testing and amalgamation routine (5-STAR) approach in which a pre-specified treatment-blinded algorithm is applied to survival times from the trial to partition patients into well-separated risk strata using baseline covariates determined to be jointly strongly prognostic for event risk. After treatment unblinding, a treatment comparison is done within each risk stratum and stratum-level results are averaged for overall inference. For illustration, we use 5-STAR to reanalyze data for the primary and key secondary time-to-event endpoints from three published cardiovascular outcomes trials. The results show that the 5-STAR estimate is typically smaller (i.e. more in favor of 5-STAR the test treatment) than the originally reported (traditional) estimate. This is not surprising because 5-STAR mitigates the presumed dilution bias in the traditional hazard ratio estimate caused by no or inadequate risk stratification, as evidenced by two detailed examples. Pre-selection of stratification factors at the trial design stage to achieve adequate risk stratification for the analysis will often be challenging. In such settings, an objective risk stratification approach such as 5-STAR, which is partly aligned with guidance from the US Food and Drug Administration on covariate-adjustment in clinical trials, is worthy of consideration.
      Citation: Clinical Trials
      PubDate: 2024-02-02T12:02:09Z
      DOI: 10.1177/17407745231222448
       
  • Research encouraging off-label use of quetiapine: A systematic
           meta-epidemiological analysis

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      Authors: Peter Grabitz, Lana Saksone, Susanne Gabriele Schorr, Johannes Schwietering, Merlin Bittlinger, Jonathan Kimmelman
      Abstract: Clinical Trials, Ahead of Print.
      Background:Researchers often conduct small studies on testing a drug’s efficacy in off-label indications. If positive results from these exploratory studies are not followed up by larger, randomized, double-blinded trials, physicians cannot be sure of a drug’s clinical value. This may lead to off-label prescriptions of ineffective treatments. We aim to describe the way clinical studies fostered off-label prescription of the antipsychotic drug quetiapine (Seroquel).Methods:In this systematic meta-epidemiological analysis, we searched EMBASE, MEDLINE, Cochrane CENTRAL and PsycINFO databases and included clinical studies testing quetiapine for unapproved indications between May 1995 and May 2022. We then assessed the frequency with which publications providing low-level evidence suggesting efficacy of quetiapine for off-label indications was not followed up by large, randomized and double-blinded trials within 5 years.Results:In total, 176 published studies were identified that reported potential efficacy of quetiapine in at least 26 indications. Between 2000 and 2007, publication of exploratory studies suggesting promise for off-label indications rapidly outpaced publication of confirmatory trials. In the 24 indications with a minimum of 5 years of follow-up from the first positive exploratory study, 19 (79%) were not followed up with large confirmatory trials within 5 years. At least nine clinical practice guidelines recommend the use of quetiapine for seven off-label indications in which published confirmatory evidence is lacking.Conclusion:Many small, post-approval studies suggested the promise of quetiapine for numerous off-label indications. These findings generally went unconfirmed in large, blinded, randomized trials years after first being published. The imbalance of exploratory and confirmatory studies likely encourages ineffective off-label treatment.
      Citation: Clinical Trials
      PubDate: 2024-01-29T11:52:04Z
      DOI: 10.1177/17407745231225470
       
  • Statistical and practical considerations in planning and conduct of
           dose-optimization trials

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      Authors: Ying Yuan, Heng Zhou, Suyu Liu
      Abstract: Clinical Trials, Ahead of Print.
      The U.S. Food and Drug Administration launched Project Optimus with the aim of shifting the paradigm of dose-finding and selection toward identifying the optimal biological dose that offers the best balance between benefit and risk, rather than the maximum tolerated dose. However, achieving dose optimization is a challenging task that involves a variety of factors and is considerably more complicated than identifying the maximum tolerated dose, both in terms of design and implementation. This article provides a comprehensive review of various design strategies for dose-optimization trials, including phase 1/2 and 2/3 designs, and highlights their respective advantages and disadvantages. In addition, practical considerations for selecting an appropriate design and planning and executing the trial are discussed. The article also presents freely available software tools that can be utilized for designing and implementing dose-optimization trials. The approaches and their implementation are illustrated through real-world examples.
      Citation: Clinical Trials
      PubDate: 2024-01-20T05:29:25Z
      DOI: 10.1177/17407745231207085
       
  • The symbolic two-step method applied to cancer care delivery research:
           Safeguarding against designing an underpowered cluster randomized trial
           with a continuous outcome by accounting for the imprecision in the within-
           and between-center variation

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      Authors: David Zahrieh, Blaize W Kandler, Jennifer Le-Rademacher
      Abstract: Clinical Trials, Ahead of Print.
      Background:Knowing the predictive factors of the variation in a center-level continuous outcome of interest is valuable in the design and analysis of parallel-arm cluster randomized trials. The symbolic two-step method for sample size planning that we present incorporates this knowledge while simultaneously accounting for patient-level characteristics. Our approach is illustrated through application to cluster randomized trials in cancer care delivery research. The required number of centers (clusters) depends on the between- and within-center variance; the within-center variance is a function of estimates obtained by regressing the log within-center variance on predictive factors. Obtaining accurate estimates of the components needed to characterize the within-center variation is challenging.Methods:Using our previously derived sample size formula, our objective in the current research is to directly account for the imprecision in these estimates, using a Bayesian approach, to safeguard against designing an underpowered study when using the symbolic two-step method. Using estimates of the required components, including the number of centers that contribute to those estimates, we make formal allowance for the imprecision in these estimates on which a sample size will be based.Results:The mean of the distribution for power is consistently smaller than the single point estimate that the sample size formula yields. The reduction in power is more pronounced in the presence of increased uncertainty about the estimates with the reduction becoming more attenuated with increased numbers of centers that contribute to the estimates.Conclusions:Accounting for imprecision in the estimates of the components required for sample size estimation using the symbolic two-step method in the design of a cluster randomized trial yields conservative estimates of power.
      Citation: Clinical Trials
      PubDate: 2024-01-20T05:28:30Z
      DOI: 10.1177/17407745231219680
       
  • Adaptive Bayesian information borrowing methods for finding and optimizing
           subgroup-specific doses

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      Authors: Jingyi Zhang, Ruitao Lin, Xin Chen, Fangrong Yan
      Abstract: Clinical Trials, Ahead of Print.
      In precision oncology, integrating multiple cancer patient subgroups into a single master protocol allows for the simultaneous assessment of treatment effects in these subgroups and promotes the sharing of information between them, ultimately reducing sample sizes and costs and enhancing scientific validity. However, the safety and efficacy of these therapies may vary across different subgroups, resulting in heterogeneous outcomes. Therefore, identifying subgroup-specific optimal doses in early-phase clinical trials is crucial for the development of future trials. In this article, we review various innovative Bayesian information-borrowing strategies that aim to determine and optimize subgroup-specific doses. Specifically, we discuss Bayesian hierarchical modeling, Bayesian clustering, Bayesian model averaging or selection, pairwise borrowing, and other relevant approaches. By employing these Bayesian information-borrowing methods, investigators can gain a better understanding of the intricate relationships between dose, toxicity, and efficacy in each subgroup. This increased understanding significantly improves the chances of identifying an optimal dose tailored to each specific subgroup. Furthermore, we present several practical recommendations to guide the design of future early-phase oncology trials involving multiple subgroups when using the Bayesian information-borrowing methods.
      Citation: Clinical Trials
      PubDate: 2024-01-20T05:28:26Z
      DOI: 10.1177/17407745231212193
       
  • A Bayesian adaptive design approach for stepped-wedge cluster randomized
           trials

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      Authors: Jijia Wang, Jing Cao, Chul Ahn, Song Zhang
      Abstract: Clinical Trials, Ahead of Print.
      Background:The Bayesian group sequential design has been applied widely in clinical studies, especially in Phase II and III studies. It allows early termination based on accumulating interim data. However, to date, there lacks development in its application to stepped-wedge cluster randomized trials, which are gaining popularity in pragmatic trials conducted by clinical and health care delivery researchers.Methods:We propose a Bayesian adaptive design approach for stepped-wedge cluster randomized trials, which makes adaptive decisions based on the predictive probability of declaring the intervention effective at the end of study given interim data. The Bayesian models and the algorithms for posterior inference and trial conduct are presented.Results:We present how to determine design parameters through extensive simulations to achieve desired operational characteristics. We further evaluate how various design factors, such as the number of steps, cluster size, random variability in cluster size, and correlation structures, impact trial properties, including power, type I error, and the probability of early stopping. An application example is presented.Conclusion:This study presents the incorporation of Bayesian adaptive strategies into stepped-wedge cluster randomized trials design. The proposed approach provides the flexibility to stop the trial early if substantial evidence of efficacy or futility is observed, improving the flexibility and efficiency of stepped-wedge cluster randomized trials.
      Citation: Clinical Trials
      PubDate: 2024-01-19T11:45:23Z
      DOI: 10.1177/17407745231221438
       
  • Adaptive phase I–II clinical trial designs identifying optimal
           biological doses for targeted agents and immunotherapies

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      Authors: Yong Zang, Beibei Guo, Yingjie Qiu, Hao Liu, Mateusz Opyrchal, Xiongbin Lu
      Abstract: Clinical Trials, Ahead of Print.
      Targeted agents and immunotherapies have revolutionized cancer treatment, offering promising options for various cancer types. Unlike traditional therapies the principle of “more is better” is not always applicable to these new therapies due to their unique biomedical mechanisms. As a result, various phase I–II clinical trial designs have been proposed to identify the optimal biological dose that maximizes the therapeutic effect of targeted therapies and immunotherapies by jointly monitoring both efficacy and toxicity outcomes. This review article examines several innovative phase I–II clinical trial designs that utilize accumulated efficacy and toxicity outcomes to adaptively determine doses for subsequent patients and identify the optimal biological dose, maximizing the overall therapeutic effect. Specifically, we highlight three categories of phase I–II designs: efficacy-driven, utility-based, and designs incorporating multiple efficacy endpoints. For each design, we review the dose–outcome model, the definition of the optimal biological dose, the dose-finding algorithm, and the software for trial implementation. To illustrate the concepts, we also present two real phase I–II trial examples utilizing the EffTox and ISO designs. Finally, we provide a classification tree to summarize the designs discussed in this article.
      Citation: Clinical Trials
      PubDate: 2024-01-11T10:03:41Z
      DOI: 10.1177/17407745231220661
       
  • Hierarchical Bayesian modeling of heterogeneous outcome variance in
           cluster randomized trials

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      Authors: Guangyu Tong, Jiaqi Tong, Yi Jiang, Denise Esserman, Michael O Harhay, Joshua L Warren
      Abstract: Clinical Trials, Ahead of Print.
      Background:Heterogeneous outcome correlations across treatment arms and clusters have been increasingly acknowledged in cluster randomized trials with binary endpoints, where analytical methods have been developed to study such heterogeneity. However, cluster-specific outcome variances and correlations have yet to be studied for cluster randomized trials with continuous outcomes.Methods:This article proposes models fitted in the Bayesian setting with hierarchical variance structure to quantify heterogeneous variances across clusters and explain it with cluster-level covariates when the outcome is continuous. The models can also be extended to analyzing heterogeneous variances in individually randomized group treatment trials, with arm-specific cluster-level covariates, or in partially nested designs. Simulation studies are carried out to validate the performance of the newly introduced models across different settings.Results:Simulations showed that overall the newly introduced models have good performance, reporting low bias and approximately 95% coverage for the intraclass correlation coefficients and regression parameters in the variance model. When variances are heterogeneous, our proposed models had improved model fit over models with homogeneous variances. When used to analyze data from the Kerala Diabetes Prevention Program study, our models identified heterogeneous variances and intraclass correlation coefficients across clusters and examined cluster-level characteristics associated with such heterogeneity.Conclusion:We proposed new hierarchical Bayesian variance models to accommodate cluster-specific variances in cluster randomized trials. The newly developed methods inform the understanding of how an intervention strategy is implemented and disseminated differently across clusters and can help improve future trial design.
      Citation: Clinical Trials
      PubDate: 2024-01-10T12:37:44Z
      DOI: 10.1177/17407745231222018
       
 
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  Subjects -> PHARMACY AND PHARMACOLOGY (Total: 575 journals)
Showing 1 - 200 of 253 Journals sorted by number of followers
Nature Reviews Drug Discovery     Full-text available via subscription   (Followers: 352)
Journal of Clinical Oncology     Hybrid Journal   (Followers: 260)
International Journal of Drug Policy     Hybrid Journal   (Followers: 241)
Journal of Medicinal Chemistry     Hybrid Journal   (Followers: 167)
Drugs     Full-text available via subscription   (Followers: 146)
Journal of Pharmaceutical Sciences     Hybrid Journal   (Followers: 140)
Advanced Drug Delivery Reviews     Hybrid Journal   (Followers: 95)
European Journal of Pharmaceutical Sciences     Hybrid Journal   (Followers: 84)
Drug Safety     Full-text available via subscription   (Followers: 81)
Pharmaceutical Research     Hybrid Journal   (Followers: 69)
Drug Discovery Today     Full-text available via subscription   (Followers: 63)
Biomaterials     Hybrid Journal   (Followers: 54)
Annals of Pharmacotherapy     Hybrid Journal   (Followers: 52)
Clinical Pharmacology & Therapeutics     Hybrid Journal   (Followers: 31)
Pharmacoepidemiology and Drug Safety     Hybrid Journal   (Followers: 29)
AAPS Journal     Hybrid Journal   (Followers: 29)
Annual Review of Pharmacology and Toxicology     Full-text available via subscription   (Followers: 27)
Journal of Pain & Palliative Care Pharmacotherapy     Hybrid Journal   (Followers: 26)
Regulatory Toxicology and Pharmacology     Hybrid Journal   (Followers: 26)
British Journal of Clinical Pharmacology     Hybrid Journal   (Followers: 25)
Journal of Controlled Release     Hybrid Journal   (Followers: 25)
Drug Development and Industrial Pharmacy     Hybrid Journal   (Followers: 25)
International Journal of Pharmacy Practice     Full-text available via subscription   (Followers: 24)
European Journal of Pharmaceutics and Biopharmaceutics     Hybrid Journal   (Followers: 23)
International Journal of Pharmaceutics     Hybrid Journal   (Followers: 23)
Journal of Clinical Psychopharmacology     Hybrid Journal   (Followers: 23)
Journal of Pharmacy and Pharmacology     Full-text available via subscription   (Followers: 23)
Critical Reviews in Toxicology     Hybrid Journal   (Followers: 22)
PharmacoEconomics     Full-text available via subscription   (Followers: 21)
American Journal of Cardiovascular Drugs     Hybrid Journal   (Followers: 20)
Chemical Research in Toxicology     Hybrid Journal   (Followers: 20)
Trends in Pharmacological Sciences     Full-text available via subscription   (Followers: 17)
Clinical Toxicology     Hybrid Journal   (Followers: 17)
Clinical Pharmacokinetics     Full-text available via subscription   (Followers: 16)
Critical Reviews in Clinical Laboratory Sciences     Hybrid Journal   (Followers: 16)
Journal of Natural Products     Hybrid Journal   (Followers: 16)
Journal of Pharmacokinetics and Pharmacodynamics     Hybrid Journal   (Followers: 16)
Toxicology and Applied Pharmacology     Hybrid Journal   (Followers: 16)
Pharmaceutical Development and Technology     Hybrid Journal   (Followers: 16)
Journal of Applied Toxicology     Hybrid Journal   (Followers: 15)
Psychopharmacology     Hybrid Journal   (Followers: 15)
Journal of Clinical Pharmacy and Therapeutics     Hybrid Journal   (Followers: 14)
Journal of Oncology Pharmacy Practice     Hybrid Journal   (Followers: 14)
Journal of the American Pharmacists Association     Full-text available via subscription   (Followers: 14)
Toxicology     Hybrid Journal   (Followers: 14)
International Journal of Toxicology     Hybrid Journal   (Followers: 13)
Journal of Pharmaceutical and Biomedical Analysis     Hybrid Journal   (Followers: 13)
Journal of Pharmacy Practice     Hybrid Journal   (Followers: 13)
Biopharmaceutics and Drug Disposition     Hybrid Journal   (Followers: 12)
Cardiovascular Drugs and Therapy     Hybrid Journal   (Followers: 12)
Clinical Trials     Hybrid Journal   (Followers: 12)
Toxicology Letters     Hybrid Journal   (Followers: 12)
Drug and Chemical Toxicology     Hybrid Journal   (Followers: 12)
American Journal of Therapeutics     Hybrid Journal   (Followers: 11)
Basic & Clinical Pharmacology & Toxicology     Hybrid Journal   (Followers: 11)
European Journal of Clinical Pharmacology     Hybrid Journal   (Followers: 11)
Journal of Psychopharmacology     Hybrid Journal   (Followers: 11)
Pharmacy Education     Full-text available via subscription   (Followers: 11)
Clinical Therapeutics     Hybrid Journal   (Followers: 10)
CNS Drugs     Full-text available via subscription   (Followers: 10)
Pharmaceutical Statistics     Hybrid Journal   (Followers: 10)
Seminars in Oncology Nursing     Full-text available via subscription   (Followers: 10)
Journal of Clinical Pharmacology     Hybrid Journal   (Followers: 10)
Toxicological Sciences     Hybrid Journal   (Followers: 10)
Advances in Pharmacological and Pharmaceutical Sciences     Open Access   (Followers: 10)
Biochemical Pharmacology     Hybrid Journal   (Followers: 9)
ChemMedChem     Hybrid Journal   (Followers: 9)
Drug Metabolism and Disposition     Hybrid Journal   (Followers: 9)
Drugs & Aging     Full-text available via subscription   (Followers: 9)
Drugs & Therapy Perspectives     Full-text available via subscription   (Followers: 9)
Medicinal Chemistry     Hybrid Journal   (Followers: 9)
Seminars in Hematology     Hybrid Journal   (Followers: 9)
Current Opinion in Pharmacology     Hybrid Journal   (Followers: 8)
Current Pharmaceutical Biotechnology     Hybrid Journal   (Followers: 8)
Drug Development Research     Hybrid Journal   (Followers: 8)
Epilepsy Research     Hybrid Journal   (Followers: 8)
European Neuropsychopharmacology     Hybrid Journal   (Followers: 8)
Food Additives & Contaminants Part A     Hybrid Journal   (Followers: 8)
Progress in Neuro-Psychopharmacology and Biological Psychiatry     Hybrid Journal   (Followers: 8)
Toxicology in Vitro     Hybrid Journal   (Followers: 8)
Inhalation Toxicology     Hybrid Journal   (Followers: 8)
Antiviral Research     Hybrid Journal   (Followers: 7)
Current Medicinal Chemistry     Hybrid Journal   (Followers: 7)
Drug Delivery     Open Access   (Followers: 7)
Environmental Toxicology and Pharmacology     Hybrid Journal   (Followers: 7)
Experimental and Clinical Psychopharmacology     Full-text available via subscription   (Followers: 7)
Frontiers in Drug Design & Discovery     Hybrid Journal   (Followers: 7)
Journal of Veterinary Pharmacology and Therapeutics     Hybrid Journal   (Followers: 7)
Prescriber     Hybrid Journal   (Followers: 7)
Toxicology Mechanisms and Methods     Hybrid Journal   (Followers: 7)
Journal of Pharmaceutical Innovation     Hybrid Journal   (Followers: 7)
AAPS PharmSciTech     Hybrid Journal   (Followers: 6)
Biometrical Journal     Hybrid Journal   (Followers: 6)
Clinical Drug Investigation     Full-text available via subscription   (Followers: 6)
Current Drug Delivery     Hybrid Journal   (Followers: 6)
Anti-Inflammatory & Anti-Allergy Agents in Medicinal Chemistry     Hybrid Journal   (Followers: 6)
Expert Review of Pharmacoeconomics & Outcomes Research     Full-text available via subscription   (Followers: 6)
Human & Experimental Toxicology     Hybrid Journal   (Followers: 6)
Toxicology and Industrial Health     Hybrid Journal   (Followers: 6)
Current Cancer Therapy Reviews     Hybrid Journal   (Followers: 5)
Current Drug Discovery Technologies     Hybrid Journal   (Followers: 5)
Anti-Infective Agents     Hybrid Journal   (Followers: 5)
Current Therapeutic Research     Open Access   (Followers: 5)
Reviews of Physiology, Biochemistry and Pharmacology     Hybrid Journal   (Followers: 5)
Expert Review of Anti-infective Therapy     Full-text available via subscription   (Followers: 5)
Expert Review of Molecular Diagnostics     Full-text available via subscription   (Followers: 5)
Fitoterapia     Hybrid Journal   (Followers: 5)
Journal of Pain Management & Medicine     Open Access   (Followers: 5)
Journal of Cardiovascular Pharmacology and Therapeutics     Hybrid Journal   (Followers: 5)
Journal of Separation Science     Hybrid Journal   (Followers: 5)
Scandinavian Journal of Clinical and Laboratory Investigation     Hybrid Journal   (Followers: 5)
Clinical Research and Regulatory Affairs     Hybrid Journal   (Followers: 5)
Pharmacogenomics Journal     Hybrid Journal   (Followers: 5)
ASSAY and Drug Development Technologies     Hybrid Journal   (Followers: 4)
BioDrugs     Full-text available via subscription   (Followers: 4)
Cancer Chemotherapy and Pharmacology     Hybrid Journal   (Followers: 4)
Current Pharmaceutical Design     Hybrid Journal   (Followers: 4)
Expert Review of Cardiovascular Therapy     Full-text available via subscription   (Followers: 4)
International Journal of Pharmaceutical and Healthcare Marketing     Hybrid Journal   (Followers: 4)
Pharmaceutical Medicine     Full-text available via subscription   (Followers: 4)
Journal of Child and Adolescent Psychopharmacology     Hybrid Journal   (Followers: 4)
Journal of Infection and Chemotherapy     Hybrid Journal   (Followers: 4)
Journal of Labelled Compounds and Radiopharmaceuticals     Hybrid Journal   (Followers: 4)
Journal of Pharmacology and Experimental Therapeutics     Hybrid Journal   (Followers: 4)
Neuropharmacology     Hybrid Journal   (Followers: 4)
Planta Medica     Hybrid Journal   (Followers: 4)
Immunopharmacology and Immunotoxicology     Hybrid Journal   (Followers: 4)
Physiology International     Full-text available via subscription   (Followers: 3)
Archiv der Pharmazie     Hybrid Journal   (Followers: 3)
BMC Pharmacology     Open Access   (Followers: 3)
Cardiovascular Therapeutics     Open Access   (Followers: 3)
Clinical and Experimental Pharmacology and Physiology     Hybrid Journal   (Followers: 3)
CNS Drug Reviews     Open Access   (Followers: 3)
Current Drug Metabolism     Hybrid Journal   (Followers: 3)
Current Pharmacogenomics and Personalized Medicine     Hybrid Journal   (Followers: 3)
Drug Resistance Updates     Hybrid Journal   (Followers: 3)
European Journal of Pharmacology     Hybrid Journal   (Followers: 3)
Frontiers in Medicinal Chemistry     Hybrid Journal   (Followers: 3)
Human Psychopharmacology Clinical and Experimental     Hybrid Journal   (Followers: 3)
Inflammation Research     Hybrid Journal   (Followers: 3)
Investigational New Drugs     Hybrid Journal   (Followers: 3)
Journal of Aerosol Medicine and Pulmonary Drug Delivery     Hybrid Journal   (Followers: 3)
Journal of Cardiovascular Pharmacology     Hybrid Journal   (Followers: 3)
Journal of Ethnopharmacology     Hybrid Journal   (Followers: 3)
Journal of Medical Marketing     Hybrid Journal   (Followers: 3)
Journal of Pharmacological and Toxicological Methods     Hybrid Journal   (Followers: 3)
Microbial Drug Resistance     Hybrid Journal   (Followers: 3)
International Journal of Neuropsychopharmacology     Open Access   (Followers: 3)
Therapeutic Drug Monitoring     Hybrid Journal   (Followers: 3)
Drug Metabolism Reviews     Hybrid Journal   (Followers: 3)
Acta Pharmacologica Sinica     Hybrid Journal   (Followers: 2)
Behavioural Pharmacology     Hybrid Journal   (Followers: 2)
Biomedicine & Pharmacotherapy     Full-text available via subscription   (Followers: 2)
Clinical Neuropharmacology     Hybrid Journal   (Followers: 2)
Current Drug Therapy     Hybrid Journal   (Followers: 2)
Current Enzyme Inhibition     Hybrid Journal   (Followers: 2)
Drugs in R & D     Full-text available via subscription   (Followers: 2)
Inflammopharmacology     Hybrid Journal   (Followers: 2)
Inpharma Weekly     Full-text available via subscription   (Followers: 2)
International Clinical Psychopharmacology     Hybrid Journal   (Followers: 2)
International Immunopharmacology     Hybrid Journal   (Followers: 2)
Letters in Drug Design & Discovery     Hybrid Journal   (Followers: 2)
Medicinal Research Reviews     Hybrid Journal   (Followers: 2)
Pharmacology & Therapeutics     Hybrid Journal   (Followers: 2)
Pharmacology Biochemistry and Behavior     Hybrid Journal   (Followers: 2)
Pharmacopsychiatry     Hybrid Journal   (Followers: 2)
Pulmonary Pharmacology & Therapeutics     Hybrid Journal   (Followers: 2)
Research in Social and Administrative Pharmacy     Hybrid Journal   (Followers: 2)
The Brown University Psychopharmacology Update     Hybrid Journal   (Followers: 2)
Toxicological & Environmental Chemistry     Hybrid Journal   (Followers: 2)
Toxicon     Hybrid Journal   (Followers: 2)
Journal of Microencapsulation: Microcapsules, Liposomes, Nanoparticles, Microcells, Microspheres     Hybrid Journal   (Followers: 2)
Canadian Journal of Physiology and Pharmacology     Hybrid Journal   (Followers: 1)
Current Neuropharmacology     Hybrid Journal   (Followers: 1)
Current Pharmaceutical Analysis     Hybrid Journal   (Followers: 1)
Current Vascular Pharmacology     Hybrid Journal   (Followers: 1)
Fundamental & Clinical Pharmacology     Hybrid Journal   (Followers: 1)
Journal of Drug Targeting     Hybrid Journal   (Followers: 1)
Journal of Inflammation     Open Access   (Followers: 1)
Journal of Neuroimmune Pharmacology     Hybrid Journal   (Followers: 1)
Journal of Texture Studies     Hybrid Journal   (Followers: 1)
Pharmacogenetics and Genomics     Hybrid Journal   (Followers: 1)
Particulate Science and Technology: An International Journal     Hybrid Journal   (Followers: 1)
Pharmaceutical Biology     Open Access  
Journal of Liposome Research     Hybrid Journal  
Vascular Pharmacology     Hybrid Journal  
Toxin Reviews     Hybrid Journal  
Kaohsiung Journal of Medical Sciences     Open Access  
Redox Report     Open Access  
Pharmacological Research     Hybrid Journal  
PharmacoEconomics & Outcomes News     Full-text available via subscription  
Pharmaceutical Chemistry Journal     Hybrid Journal  
NeuroMolecular Medicine     Hybrid Journal  
Journal of Ocular Pharmacology and Therapeutics     Hybrid Journal  
Harm Reduction Journal     Open Access  
Current Nanoscience     Hybrid Journal  
Infectious Disorders - Drug Targets     Hybrid Journal  
Current Bioactive Compounds     Hybrid Journal  
Cancer Biotherapy & Radiopharmaceuticals     Hybrid Journal  
Autonomic & Autacoid Pharmacology     Hybrid Journal  

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