JournalTOCs

Clinical Trials
Journal Prestige (SJR): 2.399

Citation Impact (citeScore): 2
Number of Followers: 12

Hybrid journal (It can contain Open Access articles)
ISSN (Print) 1740-7745 - ISSN (Online) 1740-7753
Published by Sage Publications

[1176 journals]

Estimands in clinical trials of complex disease processes
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  Authors: Richard J Cook, Jerald F Lawless
  Abstract: Clinical Trials, Ahead of Print.
  Clinical trials with random assignment of treatment provide evidence about causal effects of an experimental treatment compared to standard care. However, when disease processes involve multiple types of possibly semi-competing events, specification of target estimands and causal inferences can be challenging. Intercurrent events such as study withdrawal, the introduction of rescue medication, and death further complicate matters. There has been much discussion about these issues in recent years, but guidance remains ambiguous. Some recommended approaches are formulated in terms of hypothetical settings that have little bearing in the real world. We discuss issues in formulating estimands, beginning with intercurrent events in the context of a linear model and then move on to more complex disease history processes amenable to multistate modeling. We elucidate the meaning of estimands implicit in some recommended approaches for dealing with intercurrent events and highlight the disconnect between estimands formulated in terms of potential outcomes and the real world.
  Citation: Clinical Trials
  PubDate: 2024-08-24T07:09:52Z
  DOI: 10.1177/17407745241268054
Individualized clinical decisions within standard-of-care pragmatic
clinical trials: Implications for consent
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  Authors: Isabel M Astrachan, James Flory, Scott YH Kim
  Abstract: Clinical Trials, Ahead of Print.
  Pragmatic clinical trials of standard-of-care interventions compare the relative merits of medical treatments already in use. Traditional research informed consent processes pose significant obstacles to these trials, raising the question of whether they may be conducted with alteration or waiver of informed consent. However, to even be eligible, such a trial in the United States must have no more than minimal research risk. We argue that standard-of-care pragmatic clinical trials can be designed to ensure that they are minimal research risk if the random assignment of an intervention in a pragmatic clinical trial can accommodate individualized, clinically motivated decision-making for each participant. Such a design will ensure that the patient-participants are not exposed to any risks beyond the clinical risks of the interventions, and thus, the trial will have minimal research risk. We explain the logic of this view by comparing three scenarios of standard-of-care pragmatic clinical trials: one with informed consent, one without informed consent, and one recently proposed design called Decision Architecture Randomization Trial. We then conclude by briefly showing that our proposal suggests a natural way to determine when to use an alteration versus a waiver of informed consent.
  Citation: Clinical Trials
  PubDate: 2024-08-16T05:07:38Z
  DOI: 10.1177/17407745241266155
Taking clinical decisions seriously in standard-of-care pragmatic clinical
trials
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  Authors: Isabel M Astrachan, James Flory, Scott YH Kim
  Abstract: Clinical Trials, Ahead of Print.
  
  Citation: Clinical Trials
  PubDate: 2024-08-16T05:06:38Z
  DOI: 10.1177/17407745241266152
Commentary on Astrachan et al. The transmutation of research risk in
pragmatic clinical trials
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  Authors: Jonathan Kimmelman
  Abstract: Clinical Trials, Ahead of Print.
  
  Citation: Clinical Trials
  PubDate: 2024-08-16T05:06:19Z
  DOI: 10.1177/17407745241266168
Strategies to promote contraception use by female volunteers in
Alzheimer’s Prevention Initiative Autosomal-Dominant Alzheimer’s
Disease (API ADAD) Colombia trial
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  Authors: Christian Bustamante, Juan F Martinez, Alexander Navarro, Margarita Lopera, Gustavo Villegas, Sindy Duque, Natalia Acosta-Baena, Silvia Ríos-Romenets, Francisco Lopera
  Abstract: Clinical Trials, Ahead of Print.
  Background/Aims:Including women of childbearing age in a clinical trial makes it necessary to consider two factors from a bioethical perspective: first, the lack of knowledge about the potential teratogenic effects of an investigational product, and also, the principle of justice not to exclude any population from the benefits of research. The most common way to address this issue is by requiring volunteers to use contraceptives before, during, and a few weeks after the clinical trial. This work presents all the strategies used to promote contraception use and prevent pregnancy during the Alzheimer’s Prevention Initiative Autosomal-Dominant Alzheimer’s Disease (API ADAD) Colombia clinical trial. Two characteristics of this trial make it of special interest for closely monitoring contraception use. One is that the trial lasted more than 7 years, and the other is that participants could be carriers of the E280A PSEN1 mutation, leading to a mild cognitive impairment as early as their late 30s.Methods:An individual medical evaluation to select the contraception method that best fits the volunteer was carried out during the screening visit, remitting to the gynecologist when necessary. All non-surgical contraception methods were supplied by the sponsor. Staff were trained on contraception counseling, correctly dispensing contraceptive drugs to volunteers, and identifying, reporting, and following up on pregnancies. Two comprehensive educational campaigns on contraception use were performed, and the intervention included all volunteers. In addition, volunteers were asked on an annual survey to evaluate the dispensing procedure. Finally, the effectiveness of these strategies was retrospectively evaluated, comparing by extrapolation the number of pregnancies presented throughout the trial with the General Fertility Rate in Colombia.Results:A total of 159 female volunteers were recruited. All strategies were implemented as planned, even during the COVID-19 contingency. Ten pregnancies occurred during the evaluation period (2015–2021). Two were planned; the rest were associated with a potential therapeutic failure or incorrect use of contraceptive methods for a contraceptive failure of 0.49% per year. Sixty percent of pregnancies led to an abortion, either miscarriage or therapeutic abortion. However, there was not enough data to associate the pregnancy outcome with the administration of the investigational product. Finally, we observed a lower fertility rate in women participating in the trial compared to the Colombian population.Conclusion:The lower rates of contraceptive failure and the decrease in the incidence of pregnancies in women participating in the trial compared to the Colombian population across the 7 years of evaluation suggest that the strategies used in API ADAD Colombia were adequate and effective in addressing contraception use.
  Citation: Clinical Trials
  PubDate: 2024-08-15T04:28:08Z
  DOI: 10.1177/17407745241264217
Analysis of composite time-to-event endpoints in cardiovascular outcome
trials
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  Authors: Rachel Marceau West, Gregory Golm, Devan V Mehrotra
  Abstract: Clinical Trials, Ahead of Print.
  Composite time-to-event endpoints are commonly used in cardiovascular outcome trials. For example, the IMPROVE-IT trial comparing ezetimibe+simvastatin to placebo+simvastatin in 18,144 patients with acute coronary syndrome used a primary composite endpoint with five component outcomes: (1) cardiovascular death, (2) non-fatal stroke, (3) non-fatal myocardial infarction, (4) coronary revascularization ≥30 days after randomization, and (5) unstable angina requiring hospitalization. In such settings, the traditional analysis compares treatments using the observed time to the occurrence of the first (i.e. earliest) component outcome for each patient. This approach ignores information for subsequent outcome(s), possibly leading to reduced power to demonstrate the benefit of the test versus the control treatment. We use real data examples and simulations to contrast the traditional approach with several alternative approaches that use data for all the intra-patient component outcomes, not just the first.
  Citation: Clinical Trials
  PubDate: 2024-08-09T04:55:23Z
  DOI: 10.1177/17407745241267999
Statistical approaches for component-wise censored composite endpoints
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  Authors: Anne Eaton
  Abstract: Clinical Trials, Ahead of Print.
  Composite endpoints defined as the time to the earliest of two or more events are often used as primary endpoints in clinical trials. Component-wise censoring arises when different components of the composite endpoint are censored differently. We focus on a composite of death and a non-fatal event where death time is right censored and the non-fatal event time is interval censored because the event can only be detected during study visits. Such data are most often analysed using methods for right censored data, treating the time the non-fatal event was first detected as the time it occurred. This can lead to bias, particularly when the time between assessments is long. We describe several approaches for estimating the event-free survival curve and the effect of treatment on event-free survival via the hazard ratio that are specifically designed to handle component-wise censoring. We apply the methods to a randomized study of breastfeeding versus formula feeding for infants of mothers infected with human immunodeficiency virus.
  Citation: Clinical Trials
  PubDate: 2024-08-08T11:34:14Z
  DOI: 10.1177/17407745241265628
Inferences for the distribution of the duration of response in a
comparative clinical study
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  Authors: Ying Cui, Bo Huang, Lu Mao, Hajime Uno, Lee-Jen Wei, Lu Tian
  Abstract: Clinical Trials, Ahead of Print.
  Duration of response is an important endpoint used in drug development. Prolonged duration for response is often viewed as an early indication of treatment efficacy. However, there are numerous difficulties in studying the distribution of duration of response based on observed data subject to right censoring in practice. The most important obstacle is that the distribution of the duration of response is in general not identifiable in the presence of censoring due to the simple fact that there is no information on the joint distribution of time to response and time to progression beyond the largest follow-up time. In this article, we introduce the restricted duration of response as a replacement of the conventional duration of response. The distribution of restricted duration of response is estimable and we have proposed several nonparametric estimators in this article. The corresponding inference procedure and additional downstream analysis have been developed. Extensive numerical simulations have been conducted to examine the finite sample performance of the proposed estimators. It appears that a new regression-based two-step estimator for the survival function of the restricted duration of response tends to have a robust and superior performance, and we recommend its use in practice. A real data example from oncology has been used to illustrate the analysis for restricted duration of response.
  Citation: Clinical Trials
  PubDate: 2024-08-08T09:42:00Z
  DOI: 10.1177/17407745241264188
Using multistate models with clinical trial data for a deeper
understanding of complex disease processes
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  Authors: Terry M Therneau, Fang-Shu Ou
  Abstract: Clinical Trials, Ahead of Print.
  A clinical trial represents a large commitment from all individuals involved and a huge financial obligation given its high cost; therefore, it is wise to make the most of all collected data by learning as much as possible. A multistate model is a generalized framework to describe longitudinal events; multistate hazards models can treat multiple intermediate/final clinical endpoints as outcomes and estimate the impact of covariates simultaneously. Proportional hazards models are fitted (one per transition), which can be used to calculate the absolute risks, that is, the probability of being in a state at a given time, the expected number of visits to a state, and the expected amount of time spent in a state. Three publicly available clinical trial datasets, colon, myeloid, and rhDNase, in the survival package in R were used to showcase the utility of multistate hazards models. In the colon dataset, a very well-known and well-used dataset, we found that the levamisole+fluorouracil treatment extended time in the recurrence-free state more than it extended overall survival, which resulted in less time in the recurrence state, an example of the classic “compression of morbidity.” In the myeloid dataset, we found that complete response (CR) is durable, patients who received treatment B have longer sojourn time in CR than patients who received treatment A, while the mutation status does not impact the transition rate to CR but is highly influential on the sojourn time in CR. We also found that more patients in treatment A received transplants without CR, and more patients in treatment B received transplants after CR. In addition, the mutation status is highly influential on the CR to transplant transition rate. The observations that we made on these three datasets would not be possible without multistate models. We want to encourage readers to spend more time to look deeper into clinical trial data. It has a lot more to offer than a simple yes/no answer if only we, the statisticians, are willing to look for it.
  Citation: Clinical Trials
  PubDate: 2024-08-03T05:16:09Z
  DOI: 10.1177/17407745241267862
Defining estimand for the win ratio: Separate the true effect from
censoring
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  Authors: Lu Mao
  Abstract: Clinical Trials, Ahead of Print.
  The win ratio has been increasingly used in trials with hierarchical composite endpoints. While the outcomes involved and the rule for their comparisons vary with the application, there is invariably little attention to the estimand of the resulting statistic, causing difficulties in interpretation and cross-trial comparison. We make the case for articulating the estimand as a first step to win ratio analysis and establish that the root cause for its elusiveness is its intrinsic dependency on the time frame of comparison, which, if left unspecified, is set haphazardly by trial-specific censoring. From the statistical literature, we summarize two general approaches to overcome this uncertainty—a nonparametric one that pre-specifies the time frame for all comparisons, and a semiparametric one that posits a constant win ratio across all times—each with publicly available software and real examples. Finally, we discuss unsolved challenges, such as estimand construction and inference in the presence of intercurrent events.
  Citation: Clinical Trials
  PubDate: 2024-07-30T08:44:53Z
  DOI: 10.1177/17407745241259356
Challenges in designing a randomized, double-blind noninferiority trial
for treatment of acne: The SD-ACNE trial
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  Authors: John S Barbieri, Susan Ellenberg, Elizabeth Grice, Ann Tierney, Suzette Baez VanderBeek, Maryte Papadopoulos, Jennifer Mason, Anabel Mason, James Dattilo, David J Margolis
  Abstract: Clinical Trials, Ahead of Print.
  Background/aims:Excessive use of antibiotics has led to development of antibiotic resistance and other antibiotic-associated complications. Dermatologists prescribe more antibiotics per clinician than any other major specialty, with much of this use for acne. Alternative acne treatments are available but are used much less often than antibiotics, at least partially because dermatologists feel that they are less effective. Spironolactone, a hormonal therapy with antiandrogen effects that can address the hormonal pathogenesis of acne, may represent a therapeutic alternative to oral antibiotics for women with acne. However, the comparative effects of spironolactone and oral antibiotics in the treatment of acne have not been definitively studied. The Spironolactone versus Doxycycline for Acne: A Comparative Effectiveness, Noninferiority Evaluation (SD-ACNE) trial aims to answer whether spironolactone, in addition to standard topical therapy, is noninferior to doxycycline (an oral antibiotic) for women with acne. Several interesting challenges arose in the development of this study, including determining acceptability of the comparative regimens to participating dermatologists, identifying data to support a noninferiority margin, and establishing a process for unblinding participants after they completed the study while maintaining the blind for study investigators.Methods:We present the scientific and clinical rationale for the decisions made in the design of the trial, including input from key stakeholders through a Delphi consensus process.Results:The Spironolactone versus Doxycycline for Acne: A Comparative Effectiveness, Noninferiority Evaluation trial (NCT04582383) is being conducted at a range of community and academic sites in the United States. To maximize external validity and inform clinical practice, the study is designed with broad eligibility criteria and no prohibition of use of topical medications. Participants in the trial will be randomized to receive either spironolactone 100 mg/day or doxycycline hyclate 100 mg/day for 16 weeks. The primary outcome is the absolute decrease in inflammatory lesion count, and we have established a noninferiority margin of four inflammatory lesions. Secondary outcomes include the percentage of participants achieving Investigator Global Assessment success, change in quality of life, and microbiome changes and diversity.Conclusions:The Spironolactone versus Doxycycline for Acne: A Comparative Effectiveness, Noninferiority Evaluation trial will have substantial implications for the treatment of acne and antibiotic stewardship. In addition, this study will provide important information on the effect of these systemic agents on the development of changes to the microbiome and antibiotic resistance in a healthy population of patients.
  Citation: Clinical Trials
  PubDate: 2024-07-27T10:05:49Z
  DOI: 10.1177/17407745241265094
Modeling impact of inflation reduction act price negotiations on new drug
pipeline considering differential contributions of large and small
biopharmaceutical companies
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  Authors: Gregory Vaughan, Roger Du, Fred D Ledley
  Abstract: Clinical Trials, Ahead of Print.
  Background/Aims:Provisions of the Inflation Reduction Act mandating drug price negotiation by the Centers for Medicare & Medicaid Services have been criticized as a threat to pharmaceutical innovation. This study models potential impacts of the Inflation Reduction Act on drug approvals based on the differential contributions of large pharmaceutical companies and smaller biotechnology firms to clinical trials and the availability of capital.Methods:This study examined research and development expense, revenue, and new investment (sale of common and preferred stock) by public biopharmaceutical companies and sponsorship of phased clinical trials in ClinicalTrials.gov. Financial data were incorporated in a model that estimates the number of drugs in each phase and approvals from reported phase-specific costs and transition rates, proportional sponsorship of trials by companies of different size, projected reductions in research and development spending based on company size, and three scenarios by which large companies may allocate reductions in research and development spending among clinical phases: (1) research and development proportionally reduced across phases; (2) research and development disproportionally reduced in phases 2–3; and (3) research and development disproportionately reduced in phases 1–2.Results:Financial data were examined for 1378 public biopharmaceutical companies (2000–2018). Research and development expense was associated with revenue for 79 large companies with market capitalization ≥$7 billion with a 10% reduction in revenue reducing research and development expense by 8.4%. For 1299 smaller companies with market capitalization
  Citation: Clinical Trials
  PubDate: 2024-07-25T06:34:32Z
  DOI: 10.1177/17407745241259112
The ethical value of consulting community members in non-emergency trials
conducted with waivers of informed consent for research
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  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
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  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
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  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
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  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
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  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
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
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
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
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
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
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
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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