JournalTOCs

Clinical Trials
Journal Prestige (SJR): 2.399

Citation Impact (citeScore): 2
Number of Followers: 19

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

[1176 journals]

Controls, comparator arms, and designs for critical care comparative
effectiveness research: It’s complicated
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  Authors: Verity J Ford, Harvey G Klein, Robert L Danner, Willard N Applefeld, Jeffrey Wang, Irene Cortes-Puch, Peter Q Eichacker, Charles Natanson
  Abstract: Clinical Trials, Ahead of Print.
  BackgroundComparative effectiveness research is meant to determine which commonly employed medical interventions are most beneficial, least harmful, and/or most costly in a real-world setting. While the objectives for comparative effectiveness research are clear, the field has failed to develop either a uniform definition of comparative effectiveness research or an appropriate set of recommendations to provide standards for the design of critical care comparative effectiveness research trials, spurring controversy in recent years. The insertion of non-representative control and/or comparator arm subjects into critical care comparative effectiveness research trials can threaten trial subjects’ safety. Nonetheless, the broader scientific community does not always appreciate the importance of defining and maintaining critical care practices during a trial, especially when vulnerable, critically ill populations are studied. Consequently, critical care comparative effectiveness research trials sometimes lack properly constructed control or active comparator arms altogether and/or suffer from the inclusion of “unusual critical care” that may adversely affect groups enrolled in one or more arms. This oversight has led to critical care comparative effectiveness research trial designs that impair informed consent, confound interpretation of trial results, and increase the risk of harm for trial participants.Methods/ExamplesWe propose a novel approach to performing critical care comparative effectiveness research trials that mandates the documentation of critical care practices prior to trial initiation. We also classify the most common types of critical care comparative effectiveness research trials, as well as the most frequent errors in trial design. We present examples of these design flaws drawn from past and recently published trials as well as examples of trials that avoided those errors. Finally, we summarize strategies employed successfully in well-designed trials, in hopes of suggesting a comprehensive standard for the field.ConclusionFlawed critical care comparative effectiveness research trial designs can lead to unsound trial conclusions, compromise informed consent, and increase risks to research subjects, undermining the major goal of comparative effectiveness research: to inform current practice. Well-constructed control and comparator arms comprise indispensable elements of critical care comparative effectiveness research trials, key to improving the trials’ safety and to generating trial results likely to improve patient outcomes in clinical practice.
  Citation: Clinical Trials
  PubDate: 2023-08-24T10:29:56Z
  DOI: 10.1177/17407745231195094
Book review – For the common good
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  Authors: Søren Holm
  Abstract: Clinical Trials, Ahead of Print.
  
  Citation: Clinical Trials
  PubDate: 2023-08-23T09:58:15Z
  DOI: 10.1177/17407745231193140
Assessing the use of observational methods and real-world data to emulate
ongoing randomized controlled trials
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  Authors: Joshua D Wallach, Yihong Deng, Eric C Polley, Sanket S Dhruva, Jeph Herrin, Kenneth Quinto, Charu Gandotra, William Crown, Peter Noseworthy, Xiaoxi Yao, Molly Moore Jeffery, Timothy D Lyon, Joseph S Ross, Rozalina G McCoy
  Abstract: Clinical Trials, Ahead of Print.
  Background/aimsThere has been growing interest in better understanding the potential of observational research methods in medical product evaluation and regulatory decision-making. Previously, we used linked claims and electronic health record data to emulate two ongoing randomized controlled trials, characterizing the populations and results of each randomized controlled trial prior to publication of its results. Here, our objective was to compare the populations and results from the emulated trials with those of the now-published randomized controlled trials.MethodsThis study compared participants’ demographic and clinical characteristics and study results between the emulated trials, which used structured data from OptumLabs Data Warehouse, and the published PRONOUNCE and GRADE trials. First, we examined the feasibility of implementing the baseline participant characteristics included in the published PRONOUNCE and GRADE trials’ using real-world data and classified each variable as ascertainable, partially ascertainable, or not ascertainable. Second, we compared the emulated trials and published randomized controlled trials for baseline patient characteristics (concordance determined using standardized mean differences
  Citation: Clinical Trials
  PubDate: 2023-08-17T09:23:40Z
  DOI: 10.1177/17407745231193137
Site staff perspectives on communicating trial results to participants:
Cost and feasibility results from the Show RESPECT cluster randomised,
factorial, mixed-methods trial
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  Authors: Annabelle South, Julia Bailey, Barbara E Bierer, Eva Burnett, William J Cragg, Carlos Diaz-Montana, Katie Gillies, Talia Isaacs, Nalinie Joharatnam-Hogan, Claire Snowdon, Matthew R Sydes, Andrew J Copas
  Abstract: Clinical Trials, Ahead of Print.
  Background/AimsSharing trial results with participants is an ethical imperative but often does not happen. Show RESPECT (ISRCTN96189403) tested ways of sharing results with participants in an ovarian cancer trial (ISRCTN10356387). Sharing results via a printed summary improved patient satisfaction. Little is known about staff experience and the costs of communicating results with participants. We report the costs of communication approaches used in Show RESPECT and the views of site staff on these approaches.MethodsWe allocated 43 hospitals (sites) to share results with trial participants through one of eight intervention combinations (2 × 2 × 2 factorial; enhanced versus basic webpage, printed summary versus no printed summary, email list invitation versus no invitation). Questionnaires elicited data from staff involved in sharing results. Open- and closed-ended questions covered resources used to share results and site staff perspectives on the approaches used. Semi-structured interviews were conducted. Interview and free-text data were analysed thematically. The mean additional site costs per participant from each intervention were estimated jointly as main effects by linear regression.ResultsWe received questionnaires from 68 staff from 41 sites and interviewed 11 site staff. Sites allocated to the printed summary had mean total site costs of sharing results £13.71/patient higher (95% confidence interval (CI): −3.19, 30.60; p = 0.108) than sites allocated no printed summary. Sites allocated to the enhanced webpage had mean total site costs £1.91/patient higher (95% CI: −14, 18.74; p = 0.819) than sites allocated to the basic webpage. Sites allocated to the email list had costs £2.87/patient lower (95% CI: −19.70, 13.95; p = 0.731) than sites allocated to no email list. Most of these costs were staff time for mailing information and handling patients’ queries. Most site staff reported no concerns about how they had shared results (88%) and no challenges (76%). Most (83%) found it easy to answer queries from patients about the results and thought the way they were allocated to share results with participants would be an acceptable standard approach (76%), with 79% saying they would follow the same approach for future trials. There were no significant effects of the randomised interventions on these outcomes. Site staff emphasised the importance of preparing patients to receive the results, including giving opt-in/opt-out options, and the need to offer further support, particularly if the results could confuse or distress some patients.ConclusionsAdding a printed summary to a webpage (which significantly improved participant satisfaction) may increase costs to sites by ~£14/patient, which is modest in relation to the cost of trials. The Show RESPECT communication interventions were feasible to implement. This information could help future trials ensure they have sufficient resources to share results with participants.
  Citation: Clinical Trials
  PubDate: 2023-07-29T11:50:13Z
  DOI: 10.1177/17407745231186088
Assessing the representativeness of cluster randomized trials: Evidence
from two large pragmatic trials in United States nursing homes
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  Authors: Nina R Joyce, Sarah E Robertson, Ellen McCreedy, Jessica Ogarek, Edward H Davidson, Vincent Mor, Stefan Gravenstein, Issa J Dahabreh
  Abstract: Clinical Trials, Ahead of Print.
  Background/AimsWhen the randomized clusters in a cluster randomized trial are selected based on characteristics that influence treatment effectiveness, results from the trial may not be directly applicable to the target population. We used data from two large nursing home–based pragmatic cluster randomized trials to compare nursing home and resident characteristics in randomized facilities to eligible non-randomized and ineligible facilities.MethodsWe linked data from the high-dose influenza vaccine trial and the Music & Memory Pragmatic TRIal for Nursing Home Residents with ALzheimer’s Disease (METRICaL) to nursing home assessments and Medicare fee-for-service claims. The target population for the high-dose trial comprised Medicare-certified nursing homes; the target population for the METRICaL trial comprised nursing homes in one of four US-based nursing home chains. We used standardized mean differences to compare facility and individual characteristics across the three groups and logistic regression to model the probability of nursing home trial participation.ResultsIn the high-dose trial, 4476 (29%) of the 15,502 nursing homes in the target population were eligible for the trial, of which 818 (18%) were randomized. Of the 1,361,122 residents, 91,179 (6.7%) were residents of randomized facilities, 463,703 (34.0%) of eligible non-randomized facilities, and 806,205 (59.3%) of ineligible facilities. In the METRICaL trial, 160 (59%) of the 270 nursing homes in the target population were eligible for the trial, of which 80 (50%) were randomized. Of the 20,262 residents, 973 (34.4%) were residents of randomized facilities, 7431 (36.7%) of eligible non-randomized facilities, and 5858 (28.9%) of ineligible facilities. In the high-dose trial, randomized facilities differed from eligible non-randomized and ineligible facilities by the number of beds (132.5 vs 145.9 and 91.9, respectively), for-profit status (91.8% vs 66.8% and 68.8%), belonging to a nursing home chain (85.8% vs 49.9% and 54.7%), and presence of a special care unit (19.8% vs 25.9% and 14.4%). In the METRICaL trial randomized facilities differed from eligible non-randomized and ineligible facilities by the number of beds (103.7 vs 110.5 and 67.0), resource-poor status (4.6% vs 10.0% and 18.8%), and presence of a special care unit (26.3% vs 33.8% and 10.9%). In both trials, the characteristics of residents in randomized facilities were similar across the three groups.ConclusionIn both trials, facility-level characteristics of randomized nursing homes differed considerably from those of eligible non-randomized and ineligible facilities, while there was little difference in resident-level characteristics across the three groups. Investigators should assess the characteristics of clusters that participate in cluster randomized trials, not just the individuals within the clusters, when examining the applicability of trial results beyond participating clusters.
  Citation: Clinical Trials
  PubDate: 2023-07-26T11:11:19Z
  DOI: 10.1177/17407745231185055
The Targeted Agent and Profiling Utilization Registry Study: A pragmatic
clinical trial
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  Authors: Pam K Mangat, Elizabeth Garrett-Mayer, Jacqueline K Perez, Richard L Schilsky
  Abstract: Clinical Trials, Ahead of Print.
  The conceptual framework of pragmatism in clinical trials is explored using the American Society of Clinical Oncology’s pragmatic, non-randomized, phase II, multi-center basket clinical trial, the Targeted Agent and Profiling Utilization Registry Study (NCT02693535) as a model. The Targeted Agent and Profiling Utilization Registry Study aims to identify signals of drug activity when Food and Drug Administration approved drugs are matched to pre-specified genomic targets in patients with advanced cancer outside of their approved indication(s). The objectives of the study are to generate evidence of potential signals of activity in targeted therapies prescribed in an off-label setting as well as to expose and educate community cancer centers to genomic testing and precision medicine through the study protocol. The principles of pragmatic trial design can be applied across a broad spectrum of evidence-generation strategies, from explanatory trials to real-world evidence studies, and are briefly discussed. American Society of Clinical Oncology’s Targeted Agent and Profiling Utilization Registry Study falls closer to the pragmatic end of this spectrum as it seeks to assess the efficacy of Food and Drug Administration approved drugs used outside their approved indications under usual care conditions, yielding results generalizable to the population that would likely receive the intervention in practice, while still adhering to rigorous data quality standards. The Targeted Agent and Profiling Utilization Registry Study’s pragmatic objectives, characteristics, strengths, and limitations in its implementation are discussed and demonstrate that a large, multi-center, precision medicine basket trial can be mounted in the context of community practice and can generate clinically useful information with minimal burden to patients and clinical trial sites.
  Citation: Clinical Trials
  PubDate: 2023-07-25T12:21:11Z
  DOI: 10.1177/17407745231182013
Randomized controlled dose-escalation design to evaluate the safety of a
novel pharmacological cardiopulmonary resuscitation strategy
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  Authors: Sydney Benson, Demetri Yannopoulos, Tom P Aufderheide, Thomas A Murray
  Abstract: Clinical Trials, Ahead of Print.
  Background/aimsThe motivating randomized controlled phase I trial evaluates three sodium nitroprusside doses in a novel sodium nitroprusside-enhanced cardiopulmonary resuscitation strategy for improved end-organ perfusion relative to local standard of care. Sodium nitroprusside is a vasodilator with an established safety profile in other indications, whereas the local standard of care uses vasoconstrictors, typically epinephrine. The purpose of the proposed trial is to identify the highest safe dose of sodium nitroprusside in this new context as excessive doses may cause severe hypotension with compromised end-organ perfusion.MethodsThe proposed phase I trial design expands upon traditional dose-finding designs to include a randomized control arm, which is needed to assess safety through the relative increase in serum lactate on hospital admission. For guiding dose escalation, we propose and compare six Bayesian models which characterize expected serum lactate as a function of sodium nitroprusside dose and randomization group. Each model makes a different assumption about the expected change in serum lactate across control cohorts concurrently randomized with each dose. Model selection aims to minimize the expected number of times that a dose is incorrectly classified as safe or unsafe while sample size selection targets an expected number of incorrectly classified doses. Randomization is 1:1 for the initial cohort, and for subsequent cohorts is chosen to maximize the lower confidence bound.ResultsThe spike-and-slab model minimizes the expected number of times that a dose is incorrectly classified as safe or unsafe under the most scenarios in the motivating three-dose trial, but all six models exhibit relatively similar performance. A 2:1 randomization ratio for the second and third cohorts maximizes the lower confidence bound when using the spike-and-slab model. With the optimal design, on average, 70 individuals will ensure 1 incorrectly classified dose in 6 opportunities.ConclusionWe recommend that the motivating trial use the spike-and-slab model with a 1:1 randomization ratio for the initial cohort and 2:1 randomization ratio for subsequent cohorts; however, the simpler fixed effects approaches performed similarly well.
  Citation: Clinical Trials
  PubDate: 2023-07-24T10:50:35Z
  DOI: 10.1177/17407745231188443
The net benefit for time-to-event outcome in oncology clinical trials with
treatment switching
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  Authors: Musashi Fukuda, Kentaro Sakamaki, Koji Oba
  Abstract: Clinical Trials, Ahead of Print.
  BackgroundThe net benefit is an effect measure for any type of endpoint, including the time-to-event outcome, and can provide intuitive and clinically meaningful interpretation. It is defined as the probability of a randomly selected subject from the experimental arm surviving by at least a clinically relevant time longer than a randomly selected subject from the control arm. In oncology clinical trials, an intercurrent event such as treatment switching is common, which potentially causes informative censoring; nevertheless, conventional methods for the net benefit are not able to deal with it. In this study, we proposed a new estimator using the inverse probability of censoring weighting (IPCW) method and illustrated an oncology clinical trial with treatment switching (the SHIVA study) to apply the proposed method under the estimand framework.MethodsThe net benefit can be estimated using the survival functions of each treatment group. The proposed estimator was based on the survival functions estimated by the inverse probability of the censoring weighting method that can handle covariate-dependent censoring. The simulation study was undertaken to evaluate the operating characteristics of the proposed estimator under several scenarios; we varied the shapes of the survival curves, treatment effect, covariates effect on censoring, proportion of the censoring, threshold of the net benefit, and sample size. We also applied conventional methods (the scoring rules by Péron or Gehan) and the proposed method to the SHIVA study.ResultsOur simulation study showed that the proposed estimator provided less biased results under the covariate-dependent censoring than existing estimators. When applying the proposed method to the SHIVA study, we were able to estimate the net benefit by incorporating the information of the covariates with different estimand strategies to address the intercurrent event of the treatment switching. However, the estimates of the proposed method and those of the aforementioned conventional methods were similar under the hypothetical strategy.ConclusionsWe proposed a new estimator of the net benefit that can include covariates to account for the possibly informative censoring. We also provided an illustrative analysis of the proposed method for the oncology clinical trial with treatment switching using the estimand framework. Our proposed new estimator is suitable for handling the intercurrent events that can potentially cause covariate-dependent censoring.
  Citation: Clinical Trials
  PubDate: 2023-07-17T06:23:01Z
  DOI: 10.1177/17407745231186081
Informative cluster size in cluster-randomised trials: A case study from
the TRIGGER trial
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  Authors: Brennan C Kahan, Fan Li, Bryan Blette, Vipul Jairath, Andrew Copas, Michael Harhay
  Abstract: Clinical Trials, Ahead of Print.
  BackgroundRecent work has shown that cluster-randomised trials can estimate two distinct estimands: the participant-average and cluster-average treatment effects. These can differ when participant outcomes or the treatment effect depends on the cluster size (termed informative cluster size). In this case, estimators that target one estimand (such as the analysis of unweighted cluster-level summaries, which targets the cluster-average effect) may be biased for the other. Furthermore, commonly used estimators such as mixed-effects models or generalised estimating equations with an exchangeable correlation structure can be biased for both estimands. However, there has been little empirical research into whether informative cluster size is likely to occur in practice.MethodWe re-analysed a cluster-randomised trial comparing two different thresholds for red blood cell transfusion in patients with acute upper gastrointestinal bleeding to explore whether estimates for the participant- and cluster-average effects differed, to provide empirical evidence for whether informative cluster size may be present. For each outcome, we first estimated a participant-average effect using independence estimating equations, which are unbiased under informative cluster size. We then compared this to two further methods: (1) a cluster-average effect estimated using either weighted independence estimating equations or unweighted cluster-level summaries, and (2) estimates from a mixed-effects model or generalised estimating equations with an exchangeable correlation structure. We then performed a small simulation study to evaluate whether observed differences between cluster- and participant-average estimates were likely to occur even if no informative cluster size was present.ResultsFor most outcomes, treatment effect estimates from different methods were similar. However, differences of>10% occurred between participant- and cluster-average estimates for 5 of 17 outcomes (29%). We also observed several notable differences between estimates from mixed-effects models or generalised estimating equations with an exchangeable correlation structure and those based on independence estimating equations. For example, for the EQ-5D VAS score, the independence estimating equation estimate of the participant-average difference was 4.15 (95% confidence interval: −3.37 to 11.66), compared with 2.84 (95% confidence interval: −7.37 to 13.04) for the cluster-average independence estimating equation estimate, and 3.23 (95% confidence interval: −6.70 to 13.16) from a mixed-effects model. Similarly, for thromboembolic/ischaemic events, the independence estimating equation estimate for the participant-average odds ratio was 0.43 (95% confidence interval: 0.07 to 2.48), compared with 0.33 (95% confidence interval: 0.06 to 1.77) from the cluster-average estimator.ConclusionIn this re-analysis, we found that estimates from the various approaches could differ, which may be due to the presence of informative cluster size. Careful consideration of the estimand and the plausibility of assumptions underpinning each estimator can help ensure an appropriate analysis methods are used. Independence estimating equations and the analysis of cluster-level summaries (with appropriate weighting for each to correspond to either the participant-average or cluster-average treatment effect) are a desirable choice when informative cluster size is deemed possible, due to their unbiasedness in this setting.
  Citation: Clinical Trials
  PubDate: 2023-07-13T07:32:31Z
  DOI: 10.1177/17407745231186094
A randomized comparison of two-stage versus traditional one-stage consent
for a low-stakes randomized trial
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  Authors: Andrew J Vickers, Emily A Vertosick, Mia Austria, Christopher D Gaffney, Sigrid V Carlsson, Scott YH Kim, Behfar Ehdaie
  Abstract: Clinical Trials, Ahead of Print.
  Background/AimsIt has been proposed that informed consent for randomized trials should be split into two stages, with the purported advantage of decreased information overload and patient anxiety. We compared patient understanding, anxiety and decisional quality between two-stage and traditional one-stage consent.MethodsWe approached patients at an academic cancer center for a low-stakes trial of a mind–body intervention for procedural distress during prostate biopsy. Patients were randomized to hear about the trial by either one- or two-stage consent (n = 66 vs n = 59). Patient-reported outcomes included Quality of Informed Consent (0–100); general and consent-specific anxiety and decisional conflict, burden, and regret.ResultsQuality of Informed Consent scores were non-significantly superior for two-stage consent, by 0.9 points (95% confidence interval = −2.3, 4.2, p = 0.6) for objective and 1.1 points (95% CI = −4.8, 7.0, p = 0.7) for subjective understanding. Differences between groups for anxiety and decisional outcomes were similarly small. In a post hoc analysis, consent-related anxiety was lower among two-stage control patients, likely because scores were measured close to the time of biopsy in the two-stage patients receiving the experimental intervention.ConclusionTwo-stage consent maintains patient understanding of randomized trials, with some evidence of lowered patient anxiety. Further research is warranted on two-stage consent in higher-stakes settings.
  Citation: Clinical Trials
  PubDate: 2023-07-05T05:02:10Z
  DOI: 10.1177/17407745231185058
Data monitoring committees in pediatric randomized controlled trials
registered in ClinicalTrials.gov
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  Authors: Tiago Machado, Beatrice Mainoli, Daniel Caldeira, Joaquim J Ferreira, Ricardo M Fernandes
  Abstract: Clinical Trials, Ahead of Print.
  BackgroundData monitoring committees advise on clinical trial conduct through appraisal of emerging data to ensure participant safety and scientific integrity. While consideration of their use is recommended for trials performed with vulnerable populations, previous research has shown that data monitoring committees are reported infrequently in publications of pediatric randomized controlled trials. We aimed to assess the frequency of reported data monitoring committee adoption in ClinicalTrials.gov registry records and to examine the influence of key trial characteristics.MethodsWe conducted a cross-sectional data analysis of all randomized controlled trials performed exclusively in a pediatric population and registered in ClinicalTrials.gov between 2008 and 2021. We used the Access to Aggregate Content of ClinicalTrials.gov database to retrieve publicly available information on trial characteristics and data on safety results. Abstracted data included reported trial design and conduct parameters, population and intervention characteristics, reasons for prematurely halting, serious adverse events, and mortality outcomes. We performed descriptive analyses on the collected data and explored the influence of clinical, methodological, and operational trial characteristics on the reported adoption of data monitoring committees.ResultsWe identified 13,928 pediatric randomized controlled trial records, of which 39.7% reported adopting a data monitoring committee, 49.0% reported not adopting a data monitoring committee, and 11.3% did not answer on this item. While the number of registered pediatric trials has been increasing since 2008, we found no clear time trend in the reported adoption of data monitoring committees. Data monitoring committees were more common in multicenter trials (50.6% vs 36.9% for single-center), multinational trials (60.2% vs 38.7% for single-country), National Institutes of Health–funded (60.3% vs 40.1% for industry-funded or 37.5% for other funders), and placebo-controlled (47.6% vs 37.5% for other types of control groups). Data monitoring committees were also more common among trials enrolling younger participants, trials employing blinding techniques, and larger trials. Data monitoring committees were more common in trials with at least one serious adverse event (52.6% vs 38.4% for those without) as well as for trials with reported deaths (70.3% vs 38.9% for trials without reported deaths). In all, 4.9% were listed as halted prematurely, most often due to low accrual rates. Trials with a data monitoring committee were more often halted for reasons related to scientific data than trials without a data monitoring committee (15.7% vs 7.3%).ConclusionAccording to registry records, the use of data monitoring committees in pediatric randomized controlled trials was more frequent than previously reported in reviews of published trial reports. The use of data monitoring committees varied across key clinical and trial characteristics based on which their use is recommended. Data monitoring committees may still be underutilized in pediatric trials, and reporting of this item could be improved.
  Citation: Clinical Trials
  PubDate: 2023-06-27T10:23:28Z
  DOI: 10.1177/17407745231182417
Futility monitoring for randomized clinical trials with non-proportional
hazards: An optimal conditional power approach
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  Authors: Xiaofei Wang, Stephen L George
  Abstract: Clinical Trials, Ahead of Print.
  BackgroundStandard futility analyses designed for a proportional hazards setting may have serious drawbacks when non-proportional hazards are present. One important type of non-proportional hazards occurs when the treatment effect is delayed. That is, there is little or no early treatment effect but a substantial later effect.MethodsWe define optimality criteria for futility analyses in this setting and propose simple search procedures for deriving such rules in practice.ResultsWe demonstrate the advantages of the optimal rules over commonly used rules in reducing the average number of events, the average sample size, or the average study duration under the null hypothesis with minimal power loss under the alternative hypothesis.ConclusionOptimal futility rules can be derived for a non-proportional hazards setting that control the loss of power under the alternative hypothesis while maximizing the gain in early stopping under the null hypothesis.
  Citation: Clinical Trials
  PubDate: 2023-06-27T10:10:09Z
  DOI: 10.1177/17407745231181908
Chronic pain trials often exclude people with comorbid depressive
symptoms: A secondary analysis of 346 randomized controlled trials
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  Authors: Darren K Cheng, Maarij Hannan Ullah, Henry Gage, Rahim Moineddin, Abhimanyu Sud
  Abstract: Clinical Trials, Ahead of Print.
  BackgroundChronic pain and depression are common comorbid conditions, but there is limited evidence-based guidance for management of the two conditions together. In recent years, there has been an increase in the number of chronic pain randomized controlled trials that collect depression outcomes, but it is unknown how often these trials include people with depression or significant depressive symptoms. If trials do not include participants representative of real-world populations, evidence and guidance generated from these trials risk being inapplicable for large proportions of the target population, or worse, risk harm. Thus, in order to identify pathways to improve the conduct of clinical trials, the aims of this study were to (1) estimate the proportion of randomized controlled trials evaluating chronic pain interventions and reporting depression outcomes that include participants with significant depressive symptoms; and (2) assess the variability of inclusion proportions by pain type, intervention type, gender, country of origin, and publication year.MethodsStudies were extracted from an umbrella review of interventions for chronic pain that reported depression outcomes. Screening and data extraction were completed in duplicate and conflicts were resolved by a third author. Randomized controlled trials with at least 50% adult participants and validated depression scales were included, and randomized controlled trials with populations whose mean scores were at or above depression thresholds at baseline were considered to have included participants with depression.ResultsOf the 346 randomized controlled trials analyzed, 142 (41%) included participants with depression. Eight pain-type groups and nine intervention types were identified. Randomized controlled trials investigating fibromyalgia and mixed chronic pain had the highest proportion of participants with depression, whereas studies of arthritis and axial pain had among the lowest. Randomized controlled trials from the United States had a significantly lower inclusion proportion compared with non-US studies, especially for studies on arthritis. The increase in inclusion proportion by publication year was driven by the increase in fibromyalgia studies.Discussion and ConclusionThis study highlights opportunities to improve the conduct of chronic pain clinical trials. The majority of randomized controlled trials s analyzed evaluated participants without significant depressive symptoms at baseline, thus the findings synthesized in systematic reviews and subsequent guidelines are most applicable to the subset of real-world populations that do not have significant depressive symptoms. As well, systemic biases around psychological conditions and gender may be important contributors to differences in the study of depression in fibromyalgia compared with common conditions such as arthritis and axial pain. In order to better inform clinical practice, future research must intentionally include individuals with comorbid depression in trials of common chronic pain conditions, and consider methods to mitigate biases that may distort study design.
  Citation: Clinical Trials
  PubDate: 2023-06-22T09:04:04Z
  DOI: 10.1177/17407745231182010
The influence of political ideology on clinical trial knowledge,
invitation, and participation among adults in the United States
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  Authors: Henry Onyeaka, Daniel B Weber, Onyema Chido-Amajuoyi, Chioma Muoghalu, Hermioni L Amonoo
  Abstract: Clinical Trials, Ahead of Print.
  BackgroundClinical trials remain a critical component of medical innovation. Evidence suggests that individuals’ political ideologies may impact their health behaviors. However, there is a paucity of literature examining the relationship between political ideologies and clinical trial knowledge and participation.MethodsStudy data were derived from Health Information National Trends Survey 5 Cycle 4 (n = 3300), which was conducted from February to June 2020. We used participants’ characteristics to estimate the prevalence of clinical trial knowledge and participation. We used multivariable logistic regressions to investigate whether political ideology had a significant impact on clinical trial knowledge and participation. Jack-knife replicate weights were applied for population-level estimates.ResultsMost participants were White (64.2%), earned above US$50,000 (62.4%), and lived in urban areas (88.0%). About 59.2% reported having some knowledge of clinical trials, and only 8.9% had ever been invited to participate in clinical trials. A total of 37.0%, 29.5%, and 33.5% of the population endorsed moderate, liberal, and conservative political viewpoints respectively. In the adjusted logistic regression analysis, compared to conservatives, liberals (adjusted odds ratio, 1.92; 95% confidence interval, 1.31–2.80) and moderates (adjusted odds ratio, 1.43; 95% confidence interval, 1.09–1.88) had significantly greater odds of having knowledge of clinical trials. Also, liberals had higher odds of receiving invitations to participate in clinical trials (odds ratio, 1.76; 95% confidence interval, 1.08–2.85; p = 0.023) and greater odds of trial participation (odds ratio, 3.90; 95% confidence interval, 1.47–10.33; p = 0.007) compared to moderates.ConclusionsThe mechanism underlying the higher rates of clinical trial invitations to liberals is unclear and requires further comprehensive investigation. Similarly, further qualitative studies are needed to understand the attributes that promote knowledge and increased likelihood of clinical trial participation among liberals. This will provide crucial insight to help design interventions that further involve conservatives and moderates in clinical trials and scientific enterprise.
  Citation: Clinical Trials
  PubDate: 2023-06-22T09:00:54Z
  DOI: 10.1177/17407745231178790
A comparison of different population-level summary measures for randomised
trials with time-to-event outcomes, with a focus on non-inferiority trials
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  Authors: Matteo Quartagno, Tim P Morris, Duncan C Gilbert, Ruth E Langley, Matthew G Nankivell, Mahesh KB Parmar, Ian R White
  Abstract: Clinical Trials, Ahead of Print.
  BackgroundThe population-level summary measure is a key component of the estimand for clinical trials with time-to-event outcomes. This is particularly the case for non-inferiority trials, because different summary measures imply different null hypotheses. Most trials are designed using the hazard ratio as summary measure, but recent studies suggested that the difference in restricted mean survival time might be more powerful, at least in certain situations. In a recent letter, we conjectured that differences between summary measures can be explained using the concept of the non-inferiority frontier and that for a fair simulation comparison of summary measures, the same analysis methods, making the same assumptions, should be used to estimate different summary measures. The aim of this article is to make such a comparison between three commonly used summary measures: hazard ratio, difference in restricted mean survival time and difference in survival at a fixed time point. In addition, we aim to investigate the impact of using an analysis method that assumes proportional hazards on the operating characteristics of a trial designed with any of the three summary measures.MethodsWe conduct a simulation study in the proportional hazards setting. We estimate difference in restricted mean survival time and difference in survival non-parametrically, without assuming proportional hazards. We also estimate all three measures parametrically, using flexible survival regression, under the proportional hazards assumption.ResultsComparing the hazard ratio assuming proportional hazards with the other summary measures not assuming proportional hazards, relative performance varies substantially depending on the specific scenario. Fixing the summary measure, assuming proportional hazards always leads to substantial power gains compared to using non-parametric methods. Fixing the modelling approach to flexible parametric regression assuming proportional hazards, difference in restricted mean survival time is most often the most powerful summary measure among those considered.ConclusionWhen the hazards are likely to be approximately proportional, reflecting this in the analysis can lead to large gains in power for difference in restricted mean survival time and difference in survival. The choice of summary measure for a non-inferiority trial with time-to-event outcomes should be made on clinical grounds; when any of the three summary measures discussed here is equally justifiable, difference in restricted mean survival time is most often associated with the most powerful test, on the condition that it is estimated under proportional hazards.
  Citation: Clinical Trials
  PubDate: 2023-06-20T07:19:24Z
  DOI: 10.1177/17407745231181907
Use of comprehensive recruitment strategies in the glycemia reduction
approaches in diabetes: A comparative effectiveness study (GRADE)
multi-center clinical trial
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  Authors: Andrea L Cherrington, Heidi Krause-Steinrauf, Vanita Aroda, John B Buse, Basma Fattaleh, Stephen P Fortmann, Stephanie Hall, Sophia H Hox, Alexander Kuhn, Tina Killean, Amy Loveland, Lawrence S Phillips, Analyn Uy Jackson, Andrea Waltje, M Diane McKee
  Abstract: Clinical Trials, Ahead of Print.
  Background/AimsWe present and describe recruitment strategies implemented from 2013 to 2017 across 45 clinical sites in the United States, participating in the Glycemia Reduction Approaches in Diabetes: A Comparative Effectiveness Study, an unmasked, randomized controlled trial evaluating four glucose-lowering medications added to metformin in individuals with type 2 diabetes mellitus (duration of diabetes
  Citation: Clinical Trials
  PubDate: 2023-06-17T11:39:22Z
  DOI: 10.1177/17407745231175919
14th Annual University of Pennsylvania Conference on statistical issues in
clinical trials/subgroup analysis in clinical trials: Opportunities and
challenges (morning panel discussion)
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  Authors: Kit Roes, Janet Wittes
  Abstract: Clinical Trials, Ahead of Print.
  
  Citation: Clinical Trials
  PubDate: 2023-06-17T05:33:59Z
  DOI: 10.1177/17407745231175078
Pragmatic guidance for embedding pragmatic clinical trials in health
plans: Large simple trials aren’t so simple
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  Authors: Noelle M Cocoros, Jerry H Gurwitz, Mark J Cziraky, Christopher B Granger, Thomas Harkins, Kevin Haynes, Xiaojuan Li, Lauren Parlett, John D Seeger, Sonal Singh, Cheryl N McMahill-Walraven, Richard Platt
  Abstract: Clinical Trials, Ahead of Print.
  Background:There are unique opportunities related to the design and conduct of pragmatic trials embedded in health insurance plans, which have longitudinal data on member/patient demographics, dates of coverage, and reimbursed medical care, including prescription drug dispensings, vaccine administrations, behavioral healthcare encounters, and some laboratory results. Such trials can be large and efficient, using these data to identify trial-eligible patients and to ascertain outcomes.Methods:We use our experience primarily with the National Institutes of Health Pragmatic Trials Collaboratory Distributed Research Network, which comprises health plans that participate in the US Food & Drug Administration’s Sentinel System, to describe lessons learned from the conduct and planning of embedded pragmatic trials.Results:Information is available for research on more than 75 million people with commercial or Medicare Advantage health plans. We describe three studies that have used or plan to use the Network, as well as a single health plan study, from which we glean our lessons learned.Conclusions:Studies that are conducted in health plans provide much-needed evidence to drive clinically meaningful changes in care. However, there are many unique aspects of these trials that must be considered in the planning, implementation, and analytic phases. The type of trial best suited for studies embedded in health plans will be those that require large sample sizes, simple interventions that could be disseminated through health plans, and where data available to the health plan can be leveraged. These trials hold potential for substantial long-term impact on our ability to generate evidence to improve care and population health.
  Citation: Clinical Trials
  PubDate: 2023-06-16T06:22:09Z
  DOI: 10.1177/17407745231160459
BASIC: A Bayesian adaptive synthetic-control design for phase II clinical
trials
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  Authors: Liyun Jiang, Peter F Thall, Fangrong Yan, Scott Kopetz, Ying Yuan
  Abstract: Clinical Trials, Ahead of Print.
  BackgroundRandomized controlled trials are considered the gold standard for evaluating experimental treatments but often require large sample sizes. Single-arm trials require smaller sample sizes but are subject to bias when using historical control data for comparative inferences. This article presents a Bayesian adaptive synthetic-control design that exploits historical control data to create a hybrid of a single-arm trial and a randomized controlled trial.MethodsThe Bayesian adaptive synthetic control design has two stages. In stage 1, a prespecified number of patients are enrolled in a single arm given the experimental treatment. Based on the stage 1 data, applying propensity score matching and Bayesian posterior prediction methods, the usefulness of the historical control data for identifying a pseudo sample of matched synthetic-control patients for making comparative inferences is evaluated. If a sufficient number of synthetic controls can be identified, the single-arm trial is continued. If not, the trial is switched to a randomized controlled trial. The performance of The Bayesian adaptive synthetic control design is evaluated by computer simulation.ResultsThe Bayesian adaptive synthetic control design achieves power and unbiasedness similar to a randomized controlled trial but on average requires a much smaller sample size, provided that the historical control data patients are sufficiently comparable to the trial patients so that a good number of matched controls can be identified in the historical control data. Compared to a single-arm trial, The Bayesian adaptive synthetic control design yields much higher power and much smaller bias.ConclusionThe Bayesian adaptive synthetic-control design provides a useful tool for exploiting historical control data to improve the efficiency of single-arm phase II clinical trials, while addressing the problem of bias when comparing trial results to historical control data. The proposed design achieves power similar to a randomized controlled trial but may require a substantially smaller sample size.
  Citation: Clinical Trials
  PubDate: 2023-06-14T09:19:59Z
  DOI: 10.1177/17407745231176445
Data-driven strategies for increasing patient diversity in Bristol Myers
Squibb–sponsored US oncology clinical trials
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  Authors: Lorena Kuri, Sagar Setru, Gengyuan Liu, Diane Moniz Reed, David Weigand, Aparna Surampudi, Susan Berger, David Paulucci, Angshu Rai, Venkat Sethuraman, Blythe Vito, Helen Kellar-Wood, Mariann Micsinai Balan
  Abstract: Clinical Trials, Ahead of Print.
  Background/AimsDetermining whether clinical trial findings are applicable to diverse, real-world patient populations can be challenging when the full demographic characteristics of enrolled patients are not consistently reported. Here, we present the results of a descriptive analysis of racial and ethnic demographic information for patients in Bristol Myers Squibb (BMS)–sponsored oncology trials in the United States (US) and describe factors associated with increased patient diversity.MethodsBMS–sponsored oncology trials conducted at US sites with study enrollment dates between 1 January 2013 and 31 May 2021 were analyzed. Patient race/ethnicity information was self-reported in case report forms. As principal investigators (PIs) did not report their own race/ethnicity, a deep-learning algorithm (ethnicolr) was used to predict PI race/ethnicity. Trial sites were linked to counties to understand the role of county-level demographics. The impact of working with patient advocacy and community-based organizations to increase diversity in prostate cancer trials was analyzed. The magnitude of associations between patient diversity and PI diversity, US county demographics, and recruitment interventions in prostate cancer trials were assessed by bootstrapping.ResultsA total of 108 trials for solid tumors were analyzed, including 15,763 patients with race/ethnicity information and 834 unique PIs. Of the 15,763 patients, 13,968 (89%) self-reported as White, 956 (6%) Black, 466 (3%) Asian, and 373 (2%) Hispanic. Among 834 PIs, 607 (73%) were predicted to be White, 17 (2%) Black, 161 (19%) Asian, and 49 (6%) A positive concordance was observed between Hispanic patients and PIs (mean = 5.9%; 95% confidence interval (CI) = 2.4, 8.9), a less positive concordance between Black patients and PIs (mean = 1.0%; 95% CI = −2.7, 5.5), and no concordance between Asian patients and PIs. Geographic analyses showed that more non-White patients enrolled in study sites in counties with higher proportions of non-White residents (e.g. a county population that was 5%–30% Black had 7%–14% more Black patients enrolled in study sites). Following purposeful recruitment efforts in prostate cancer trials, 11% (95% CI = 7.7, 15.3) more Black men enrolled in prostate cancer trials.ConclusionMost patients in these clinical trials were White. PI diversity, geographic diversity, and recruitment efforts were related to greater patient diversity. This report constitutes an essential step in benchmarking patient diversity in BMS US oncology trials and enables BMS to understand which initiatives may increase patient diversity. While complete reporting of patient characteristics such as race/ethnicity is critical, identifying diversity improvement tactics with the highest impact is essential. Strategies with the greatest concordance to clinical trial patient diversity should be implemented to make meaningful improvements to the diversity of clinical trial populations.
  Citation: Clinical Trials
  PubDate: 2023-06-13T10:16:07Z
  DOI: 10.1177/17407745231180506
Handling intercurrent events and missing data in non-inferiority trials
using the estimand framework: A tuberculosis case study
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  Authors: Sunita Rehal, Suzie Cro, Patrick PJ Phillips, Katherine Fielding, James R Carpenter
  Abstract: Clinical Trials, Ahead of Print.
  IntroductionThe ICH E9 addendum outlining the estimand framework for clinical trials was published in 2019 but provides limited guidance around how to handle intercurrent events for non-inferiority studies. Once an estimand is defined, it is also unclear how to deal with missing values using principled analyses for non-inferiority studies.MethodsUsing a tuberculosis clinical trial as a case study, we propose a primary estimand, and an additional estimand suitable for non-inferiority studies. For estimation, multiple imputation methods that align with the estimands for both primary and sensitivity analysis are proposed. We demonstrate estimation methods using the twofold fully conditional specification multiple imputation algorithm and then extend and use reference-based multiple imputation for a binary outcome to target the relevant estimands, proposing sensitivity analyses under each. We compare the results from using these multiple imputation methods with those from the original study.ResultsConsistent with the ICH E9 addendum, estimands can be constructed for a non-inferiority trial which improves on the per-protocol/intention-to-treat-type analysis population previously advocated, involving respectively a hypothetical or treatment policy strategy to handle relevant intercurrent events. Results from using the ‘twofold’ multiple imputation approach to estimate the primary hypothetical estimand, and using reference-based methods for an additional treatment policy estimand, including sensitivity analyses to handle the missing data, were consistent with the original study’s reported per-protocol and intention-to-treat analysis in failing to demonstrate non-inferiority.ConclusionsUsing carefully constructed estimands and appropriate primary and sensitivity estimators, using all the information available, results in a more principled and statistically rigorous approach to analysis. Doing so provides an accurate interpretation of the estimand.
  Citation: Clinical Trials
  PubDate: 2023-06-06T05:16:40Z
  DOI: 10.1177/17407745231176773
Considerations for identifying the “right” subgroup in
adaptive enrichment trials
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  Authors: Noah Simon
  Abstract: Clinical Trials, Ahead of Print.
  Adaptive Enrichment Trials aim to make efficient use of data in a pivotal trial of a new targeted therapy to both (a) more precisely identify who benefits from that therapy and (b) improve the likelihood of successfully concluding that the drug is effective, while controlling the probability of false positives. There are a number of frameworks for conducting such a trial and decisions that must be made regarding how to identify that target subgroup. Among those decisions, one must choose how aggressively to restrict enrollment criteria based on the accumulating evidence in the trial. In this article, we empirically evaluate the impact of aggressive versus conservative enrollment restrictions on the power of the trial to detect an effect of treatment. We identify that, in some cases, a more aggressive strategy can substantially improve power. This additionally raises an important question regarding label indication: To what degree do we need a formal test of the hypothesis of no treatment effect in the exact population implied by the label indication' We discuss this question and evaluate how our answer for adaptive enrichment trials may relate to the answer implied by current practice for broad eligibility trials.
  Citation: Clinical Trials
  PubDate: 2023-06-03T10:02:05Z
  DOI: 10.1177/17407745231174909
Poor effort on cognitive testing in voluntary research predicts failure in
US Army Ranger School: Implications for clinical trial design
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  Authors: Travis H Turner, Jill C Newman, Bernadette P Marriott
  Abstract: Clinical Trials, Ahead of Print.
  IntroductionFailure to provide effortful performance on cognitive testing is not uncommon for participants in clinical trials and can significantly impact sensitivity to treatment effect. Whether poor effort on cognitive testing might relate to other behaviors of interest is unknown. In the current investigation, we examined whether effort on baseline cognitive testing in a randomized controlled trial to enhance resiliency in US Army Officers predicted subsequent success in Ranger school.MethodsBaseline data on six cognitive tests were obtained from 237 US Army Officers entering a military training program prior to attempting Ranger School. Participation was voluntary and the Army was not informed of test scores. “Poor effort” was defined by chance-level accuracy or extreme outlier scores. Logistic regression examined likelihood of Ranger success according to the number of tests with poor effort.ResultsOverall, 170 (72%) participants provided good effort on all tests. For these participants, 47% were successful in Ranger, versus 32% with poor effort on one test and 14% with poor effort on two tests. Logistic regression analysis found poor effort on baseline testing predicted reduced likelihood of Ranger success, β =−.486, p = .005.DiscussionA substantial number of participants exhibited poor effort on testing, and poor effort was predictive of failure in Ranger school. Findings highlight the importance of assessing effort in clinical trials involving cognitive outcomes and suggest application of cognitive effort testing in trials where other motivated behavior is targeted.RegistrationClinical Trials.gov NCT02908932.
  Citation: Clinical Trials
  PubDate: 2023-06-03T04:58:24Z
  DOI: 10.1177/17407745231178847
Perspectives of adolescents with neurofibromatosis 1 and cutaneous
neurofibromas: Implications for clinical trials
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  Authors: Ashley Cannon, Kavita Y Sarin, Andrea K Petersen, Dominique C Pichard, Pamela L Wolters, Gregg Erickson, Andrés J Lessing, Peng Li, Claas Röhl, Tena Rosser, Brigitte C Widemann, Jaishri O Blakeley, Scott R Plotkin
  Abstract: Clinical Trials, Ahead of Print.
  Background/AimsMore than 99% of individuals with neurofibromatosis 1 develop cutaneous neurofibromas, benign nerve sheath tumors that manifest as nodules on the skin. These cutaneous neurofibromas emerge with age, appearing most commonly in adolescence. Nevertheless, few data have been published on how adolescents with neurofibromatosis 1 feel about cutaneous neurofibromas. The purpose of this study was to assess the perspectives of adolescents with neurofibromatosis 1 and their caregivers regarding cutaneous neurofibroma morbidity, treatment options, and acceptable risks-benefits of treatment.MethodsAn online survey was distributed through the world’s largest NF registry. Eligibility criteria included self-reported neurofibromatosis 1 diagnosis, adolescent child ages 12–17 years, ≥1 cutaneous neurofibroma, and ability to read English. The survey was designed to collect details about the adolescent’s cutaneous neurofibromas, views on morbidity related to cutaneous neurofibromas, social and emotional impact of cutaneous neurofibromas, communication regarding cutaneous neurofibromas, and views regarding current and potential future cutaneous neurofibroma treatment.ResultsSurvey respondents included 28 adolescents and 32 caregivers. Adolescents reported having several negative feelings about cutaneous neurofibromas, particularly feeling worried about the potential progression of their cutaneous neurofibromas (50%). Pruritus (34%), location (34%), appearance (31%), and number (31%) were the most bothersome cutaneous neurofibroma features. Topical medication (77%–96%), followed by oral medication (54%–93%), was the most preferred treatment modality. Adolescents and caregivers most often replied that cutaneous neurofibroma treatment should be initiated when cutaneous neurofibromas become bothersome. The majority of respondents were willing to treat cutaneous neurofibromas for at least 1 year (64%–75%). Adolescent and caregivers were least willing to risk pain (72%–78%) and nausea/vomiting (59%–81%) as a cutaneous neurofibroma treatment side effect.ConclusionsThese data indicate that adolescents with neurofibromatosis 1 are negatively impacted by their cutaneous neurofibromas, and that both adolescents and their caregivers would be willing to try longer-term experimental treatments.
  Citation: Clinical Trials
  PubDate: 2023-06-03T04:53:54Z
  DOI: 10.1177/17407745231178839
Partner engagement for planning and development of non-pharmacological
care pathways in the AIM-Back trial
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  Authors: Lindsay A Ballengee, Heather A King, Corey Simon, Trevor A Lentz, Kelli D Allen, Catherine Stanwyck, Micaela Gladney, Steven Z George, S Nicole Hastings
  Abstract: Clinical Trials, Ahead of Print.
  Background/AimsEmbedded pragmatic clinical trials are increasingly recommended for non-pharmacological pain care research due to their focus on examining intervention effectiveness within real-world settings. Engagement with patients, health care providers, and other partners is essential, yet there is limited guidance for how to use engagement to meaningfully inform the design of interventions to be tested in pain-related pragmatic clinical trials. This manuscript aims to describe the process and impacts of partner input on the design of two interventions (care pathways) for low back pain currently being tested in an embedded pragmatic trial in the Veterans Affairs health care system.MethodsSequential cohort design for intervention development was followed. Engagement activities were conducted with 25 participants between November 2017 and June 2018. Participants included representatives from multiple groups: clinicians, administrative leadership, patients, and caregivers.ResultsPartner feedback led to several changes in each of the care pathways to improve patient experience and usability. Major changes to the sequenced care pathway included transitioning from telephone-based delivery to a flexible telehealth model, increased specificity about pain modulation activities, and reduction of physical therapy visits. Major changes to the pain navigator pathway included transitioning from a traditional stepped care model to one that offers care in a feedback loop, increased flexibility regarding pain navigator provider type, and increased specificity for patient discharge criteria. Centering patient experience emerged as a key consideration from all partner groups.ConclusionDiverse input is important to consider before implementing new interventions in embedded pragmatic trials. Partner engagement can increase acceptability of new care pathways to patients and providers and enhance uptake of effective interventions by health systems.Trial registrationNCT#04411420. Registered on 2 June 2020.
  Citation: Clinical Trials
  PubDate: 2023-06-03T04:44:14Z
  DOI: 10.1177/17407745231178789
The improving Medication Adherence in Adolescents and young adults
following Liver Transplantation (iMALT) multisite trial: Design and trial
implementation considerations
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  Authors: Eyal Shemesh, Sarah Duncan-Park, George Mazariegos, Rachel Annunziato, Ravinder Anand, Miguel Reyes-Mugica, Jeff Mitchell, Benjamin L Shneider
  Abstract: Clinical Trials, Ahead of Print.
  Background/aimsMedication non-adherence is a leading cause of transplant rejection, organ loss, and death; yet no rigorous controlled study to date has shown compelling clinical benefits from an adherence-improving intervention. Non-adherent patients are less likely to participate in trials, and therefore, most studies enroll a majority of adherent patients who do not stand to benefit from the intervention, as they do not have the condition (non-adherence) under investigation. The improving Medication Adherence in adolescent Liver Transplant recipients trial specifically targets non-adherent patients to investigate whether a remote intervention to improve adherence results in reduced incidence of biopsy-confirmed rejection.MethodsImproving Medication Adherence in adolescent Liver Transplant is a randomized single-blind controlled multisite, multinational National Institutes of Health-funded trial involving 13 pediatric transplant centers in the United States and Canada. An innovative, objective adherence biomarker—the Medication Level Variability Index, which is the standard deviation of a series of medication blood levels for each patient, is used to identify non-adherent patients at risk for rejection. The index is computed using electronic health record information for all potentially eligible patients based on repeated reviews of the entire clinic’s roster. Identified patients, after consent, are randomized to intervention versus control (treatment as usual) arms. The remote intervention is delivered for 2 years by trained interventionists who reside in various locations in the United States. The primary outcome is the incidence of biopsy-confirmed acute cellular rejection, as confirmed by a majority vote of three pathologists who are masked to the study allocation and clinical information.DiscussionImproving Medication Adherence in adolescent Liver Transplant includes several innovative design elements. The use of a validated, objective adherence index to survey a large cohort of transplant recipients allows the teams to avoid bias inherent in both convenience sampling and referral-based recruitment and enroll only patients whose computed index indicates substantially increased risk of rejection. The remote intervention paradigm helps to engage patients who are by definition hard to engage. The use of an objective, masked medical (rather than behavioral) outcome measure reduces the likelihood of biases related to clinical information and ensures broad acceptance by the field. Finally, monitoring for potential adverse events related to increased medication exposure due to the adherence intervention acknowledges that a successful intervention (increasing adherence) could have detrimental side effects via increased exposure to and potential toxicity of the medication. Such monitoring is almost never attempted in clinical trials evaluating adherence interventions.
  Citation: Clinical Trials
  PubDate: 2023-06-03T04:41:14Z
  DOI: 10.1177/17407745231176834
Now is the time to fix the clinical research workforce crisis
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  Authors: Stephanie A Freel, Denise C Snyder, Kara Bastarache, Carolynn Thomas Jones, Mark B Marchant, Laura A Rowley, Stephen A Sonstein, Karen M Lipworth, Susan P Landis
  Abstract: Clinical Trials, Ahead of Print.
  The clinical and translational research enterprise is recognized by many as the “evidence generation system.” While there have been several calls to revolutionize this enterprise to more effectively deliver the fruits of biomedical science to patients and society, significant issues across the clinical research workforce are pervasive. Perhaps the most visible sign is the widening gap between supply and demand for competent staff. Underpinning this, is a perfect storm of complex issues. Now reaching crisis point, this problem is far bigger than a staffing issue and ultimately jeopardizes the “engine” of drug and device development. With the current perilous state of the workforce, proposed enterprise fixes are likely to languish far out of reach, given that even “business as usual” is under threat. In fact, a glaring disconnect is evident between the visionary discourse on how to revolutionize the clinical research enterprise and the sober recognition that operationalization of any such vision rests on the shoulders of a workforce that’s in dire straits. In this article, we provide a brief forensic analysis of the workforce problem and an initial indication of where solutions may lie.
  Citation: Clinical Trials
  PubDate: 2023-06-02T08:55:11Z
  DOI: 10.1177/17407745231177885
Barriers and facilitators to the inclusion of deaf people in clinical
trials
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  Authors: Poorna Kushalnagar, Onudeah Nicolarakis, Gene Mirus, Melissa Anderson, Teresa Burke, Raja Kushalnagar
  Abstract: Clinical Trials, Ahead of Print.
  Background/AimsThis article discusses the barriers that prevent deaf people from participating in clinical trials and offers recommendations to overcome these barriers and ensure equal access to study participation.MethodsBetween April and May 2022, we conducted six focus groups with 20 deaf adults who use American Sign Language, all of whom had previous experience as research study participants. Focus group prompts queried community awareness of clinical trial opportunities, barriers and facilitators to deaf people’s participation in clinical trials, and recommended resources to improve clinical trial access. This qualitative focus group data is supplemented by survey data gathered from 40 principal investigators and clinical research coordinators between November 2021 and December 2021. The survey queried researchers’ prior experiences with enrolling deaf participants in clinical trials and strategies they endorse for enrollment of deaf participants in future clinical trials.ResultsFocus group participants unanimously agreed that, compared to the general hearing population, deaf sign language users lack equivalent access to clinical trial participation. Reported barriers included lack of awareness of clinical trial opportunities, mistrust of hearing researchers, and refusal by clinical trial staff to provide accessible communication (e.g. denial of requests for sign language interpreters). Survey data from 40 principal investigators and clinical research coordinators corroborated these barriers. For example, only 2 out of 40 survey respondents had ever enrolled a deaf person in a clinical trial. Respondents indicated that the most helpful strategies for including deaf sign language users in future clinical trials would be assistance with making recruitment information accessible to deaf sign language users and assistance in identifying qualified interpreters to hire to help facilitate the informed consent process.ConclusionThe lack of communication accessibility is the most common factor preventing deaf sign language users from participating in clinical trials. This article provides recommendations for hearing researchers to improve deaf people’s access to clinical trials moving forward, drawing from mixed-methods data.
  Citation: Clinical Trials
  PubDate: 2023-05-27T05:53:13Z
  DOI: 10.1177/17407745231177376
Performance of Cox regression models for composite time-to-event endpoints
with component-wise censoring in randomized trials
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  Authors: Jaime Lynn Speiser, Walter T Ambrosius, Nicholas M Pajewski
  Abstract: Clinical Trials, Ahead of Print.
  BackgroundComposite time-to-event endpoints are beneficial for assessing related outcomes jointly in clinical trials, but components of the endpoint may have different censoring mechanisms. For example, in the PRagmatic EValuation of evENTs And Benefits of Lipid-lowering in oldEr adults (PREVENTABLE) trial, the composite outcome contains one endpoint that is right censored (all-cause mortality) and two endpoints that are interval censored (dementia and persistent disability). Although Cox regression is an established method for time-to-event outcomes, it is unclear how models perform under differing component-wise censoring schemes for large clinical trial data. The goal of this article is to conduct a simulation study to investigate the performance of Cox models under different scenarios for composite endpoints with component-wise censoring.MethodsWe simulated data by varying the strength and direction of the association between treatment and outcome for the two component types, the proportion of events arising from the components of the outcome (right censored and interval censored), and the method for including the interval-censored component in the Cox model (upper value and midpoint of the interval). Under these scenarios, we compared the treatment effect estimate bias, confidence interval coverage, and power.ResultsBased on the simulation study, Cox models generally have adequate power to achieve statistical significance for comparing treatments for composite outcomes with component-wise censoring. In our simulation study, we did not observe substantive bias for scenarios under the null hypothesis or when the treatment has a similar relative effect on each component outcome. Performance was similar regardless of if the upper value or midpoint of the interval-censored part of the composite outcome was used.ConclusionCox regression is a suitable method for analysis of clinical trial data with composite time-to-event endpoints subject to different component-wise censoring mechanisms.
  Citation: Clinical Trials
  PubDate: 2023-05-27T05:48:54Z
  DOI: 10.1177/17407745231177046
An example of implementing a safety protocol in remote intervention and
survey research with college students
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  Authors: Christopher J Mehus, Brittany Stevenson, Lindsey Weiler, Meredith Gunlicks-Stoessel, Nicole Morrell, Megan E Patrick
  Abstract: Clinical Trials, Ahead of Print.
  IntroductionThis article draws attention to the need for open evaluation and reporting on safety protocols in survey and intervention research. We describe a protocol for responding to those who indicate increased risk of self-harm (i.e. suicidality or potentially lethal alcohol use) as an example and report on the outcome of our procedures.MethodsParticipants were first-year college students (n = 891) participating in an intervention trial for binge drinking. We describe the protocol, provide descriptive outcomes, and examine whether participant sex, attrition, or study intervention condition were related to endorsing items that indicated risk for suicidality or potentially lethal alcohol use.ResultsOf the 891 participants, 167 (18.7%) were identified as being at risk in one or more study wave. Of those, we were able to successfully contact 100 (59.9%), 76 (45.5%) by phone, and 24 (14.4%) by email. Of those 100, 78 accepted mental health resources as a result of outreach. Participant sex, attrition, and intervention condition were not related to risk.DiscussionThis article may aid other research teams in developing similar protocols. Strategies to reach an even greater proportion of high-risk participants are needed. A body of literature documenting published safety protocols in research and the associated outcomes would help to identify opportunities for improvement.
  Citation: Clinical Trials
  PubDate: 2023-05-27T05:40:14Z
  DOI: 10.1177/17407745231176803
Bringing data monitoring committee charters into the sunlight
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  Authors: David L DeMets, Deborah A Zarin, Frank Rockhold, Susan S Ellenberg, Thomas Fleming, Janet Wittes
  Abstract: Clinical Trials, Ahead of Print.
  Clinical trials investigating novel or high risk interventions, or studying vulnerable participants, often use a data monitoring committee to oversee the progress of the trial. The data monitoring committee serves both an ethical and a scientific function, by protecting the interests of trial participants while ensuring the integrity of the trial results. A data monitoring committee charter, which typically describes the procedures by which data monitoring committees operate, contains details about the data monitoring committee’s organizational structure, membership, meeting frequency, sequential monitoring guidelines, and the overall contents of data monitoring committee reports for interim review. These charters, however, are generally not reviewed by outside entities and are rarely publicly available. The result is that a key component of trial oversight remains in the dark. We recommend that ClinicalTrials.gov modify its system to allow uploading of data monitoring committee charters, as is already possible for other important study documents and that clinical trialists take advantage of this opportunity to voluntarily upload the data monitoring committee charter for trials that have one. The resulting cache of publicly available data monitoring committee charters should provide important insights for those interested in a particular trial, as well as for meta-researchers who wish to understand and potentially improve how this important component of trial oversight is actually being applied.
  Citation: Clinical Trials
  PubDate: 2023-05-26T06:28:40Z
  DOI: 10.1177/17407745231169499
Commentary on DeMets et al: The need for greater transparency regarding
data monitoring committee charters
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  Authors: Seema K Shah, Lisa Eckstein, Akram Ibrahim
  Abstract: Clinical Trials, Ahead of Print.
  
  Citation: Clinical Trials
  PubDate: 2023-05-26T06:28:35Z
  DOI: 10.1177/17407745231169496
Overview of modern approaches for identifying and evaluating heterogeneous
treatment effects from clinical data
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  Authors: Ilya Lipkovich, David Svensson, Bohdana Ratitch, Alex Dmitrienko
  Abstract: Clinical Trials, Ahead of Print.
  There has been much interest in the evaluation of heterogeneous treatment effects (HTE) and multiple statistical methods have emerged under the heading of personalized/precision medicine combining ideas from hypothesis testing, causal inference, and machine learning over the past 10-15 years. We discuss new ideas and approaches for evaluating HTE in randomized clinical trials and observational studies using the features introduced earlier by Lipkovich, Dmitrienko, and D’Agostino that distinguish principled methods from simplistic approaches to data-driven subgroup identification and estimating individual treatment effects and use a case study to illustrate these approaches. We identified and provided a high-level overview of several classes of modern statistical approaches for personalized/precision medicine, elucidated the underlying principles and challenges, and compared findings for a case study across different methods. Different approaches to evaluating HTEs may produce (and actually produced) highly disparate results when applied to a specific data set. Evaluating HTE with machine learning methods presents special challenges since most of machine learning algorithms are optimized for prediction rather than for estimating causal effects. An additional challenge is in that the output of machine learning methods is typically a “black box” that needs to be transformed into interpretable personalized solutions in order to gain acceptance and usability.
  Citation: Clinical Trials
  PubDate: 2023-05-19T07:06:40Z
  DOI: 10.1177/17407745231174544
Proceedings of the University of Pennsylvania 14th annual conference on
statistical issues in clinical trials: Subgroup analysis in randomized
clinical trials—Challenges and opportunities
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  Authors: Susan S Ellenberg, Jonas H Ellenberg
  Abstract: Clinical Trials, Ahead of Print.
  
  Citation: Clinical Trials
  PubDate: 2023-05-19T06:48:47Z
  DOI: 10.1177/17407745231173007
Regulatory compliance and readability of informed consent forms in
industry-sponsored drug development clinical trials
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  Authors: Nahathai Dukaew, Mingkwan Na Takuathung, Wannachai Sakuludomkan, Kanyarat Chairaksa, Preeyaporn Klinjan, Nimit Morakote, Nut Koonrungsesomboon
  Abstract: Clinical Trials, Ahead of Print.
  Background/AimsAn informed consent form is essential in drug development clinical trials. This study aimed to evaluate regulatory compliance and readability of informed consent forms currently being used in industry-sponsored drug development clinical trials.MethodsThis descriptive, cross-sectional study evaluated the informed consent forms of industry-sponsored drug development clinical trials conducted at the Faculty of Medicine, Chiang Mai University, between 2019 and 2020. The informed consent form’s compliance with the three major ethical guidelines and regulations (i.e. International Council for Harmonization of Technical Requirements for Pharmaceuticals for Human Use E6(R2) Good Clinical Practice; Declaration of Helsinki; and the revised Common Rule) were analyzed. The document length and the readability scores (using Flesch Reading Ease and Flesch-Kincaid Reading Grade) were assessed.ResultsOf 64 reviewed informed consent forms, the average page length was 22.0 ± 7.4 pages. More than half of their length was mainly devoted to three elements: trial procedures (22.9%), risks and discomforts (19.1%), and confidentiality and the limit of confidentiality (10.1%). Although most of the required elements of the informed consent form content were included in most informed consent forms, we identified four elements with often missing information in the form: aspects of research that are experimental (n = 43, 67.2%), involvement of whole-genome sequencing (n = 35, 54.7%), commercial profit sharing (n = 31, 48.4%), and posttrial provisions (n = 28, 43.8%).ConclusionThe informed consent forms in industry-sponsored drug development clinical trials were long but incomplete. Our findings draw attention to ongoing challenges in industry-sponsored drug development clinical trials, where deficient informed consent form quality continues to exist.
  Citation: Clinical Trials
  PubDate: 2023-05-17T05:55:13Z
  DOI: 10.1177/17407745231174528
Inference on subgroups identified based on a heterogeneous treatment
effect in a post hoc analysis of a clinical trial
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  Authors: Beibo Zhao, Anastasia Ivanova, Jason Fine
  Abstract: Clinical Trials, Ahead of Print.
  Due to the many benefits of understanding treatment effect heterogeneity in a clinical trial, an exploratory post hoc subgroup analysis is often performed to find subpopulations of patients with conditional average treatment effect that suggests better treatment efficacy than in the overall population. A naive re-substitution approach uses all available data to identify a subgroup and then proceeds with estimation and inference using the same data set. This approach generally leads to an overly optimistic estimate of conditional average treatment effect. In this article, in a post hoc analysis, we estimate the target optimal subgroup through maximizing a utility function, from candidates systematically identified with a penalized regression. We then compare two resampling-based bias-correction methods, cross-validation and debiasing bootstrap, for obtaining approximately unbiased estimates and valid inference of conditional average treatment effect in the identified subgroup, with either an empirical or an augmented estimator. Our results show that both the cross-validation and the debiasing bootstrap methods reduce the re-substitution bias effectively. The cross-validation method appears to have less biased point estimates, smaller standard error estimates, but poorer coverages than the debiasing bootstrap method when using the empirical estimator and the sample size is moderate. Using the augmented estimator in the debiasing bootstrap method leads to less biased point estimates but poorer coverages. We conclude that bias correction should be a part of every exploratory post hoc subgroup analysis to eliminate re-substitution bias and to obtain a proper confidence interval for the estimated conditional average treatment effect in the selected subgroup.
  Citation: Clinical Trials
  PubDate: 2023-05-12T06:00:11Z
  DOI: 10.1177/17407745231173055
14th Annual University of Pennsylvania Conference on statistical issues in
clinical trials/subgroup analysis in clinical trials: Opportunities and
challenges (afternoon panel discussion)
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  Authors: Kosuke Imai, Michael Rosenblum, Mark Rothmann
  Abstract: Clinical Trials, Ahead of Print.
  
  Citation: Clinical Trials
  PubDate: 2023-05-06T11:27:51Z
  DOI: 10.1177/17407745231169681
Overall average treatment effects from clinical trials,
one-variable-at-a-time subgroup analyses and predictive approaches to
heterogeneous treatment effects: Toward a more patient-centered
evidence-based medicine
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  Authors: David M Kent
  Abstract: Clinical Trials, Ahead of Print.
  Despite the predominance of the evidence-based medicine paradigm, a fundamental incongruity remains: Evidence is derived from groups of people, yet medical decisions are made by and for individuals. Randomization ensures the comparability of treatment groups within a clinical trial, which allows for unbiased estimation of average treatment effects. If we treated groups of patients instead of individuals, or if patients with the same disease were identical to one another in all factors that determined the harms and the benefits of therapy, then these group-level averages would make a perfectly sound foundation for medical decision-making. But patients differ from one another in many ways that determine the likelihood of an outcome, both with and without a treatment. Nevertheless, popular approaches to evidence-based medicine have encouraged a reliance on the average treatment effects estimated from clinical trials and meta-analysis as guides to decision-making for individuals. Here, we discuss the limitations of this approach as well as limitations of conventional, one-variable-at-a-time subgroup analysis; finally, we discuss the rationale for “predictive” approaches to heterogeneous treatment effects. Predictive approaches to heterogeneous treatment effects combine methods for causal inference (e.g. randomization) with methods for prediction that permit inferences about which patients are likely to benefit and which are not, taking into account multiple relevant variables simultaneously to yield “personalized” estimates of benefit–harm trade-offs. We focus on risk modeling approaches, which rely on the mathematical dependence of the absolute treatment effect with the baseline risk, which varies substantially “across patients” in most trials. While there are a number of examples of risk modeling approaches that have been practice-changing, risk modeling does not provide ideal estimates of individual treatment effects, since risk modeling does not account for how individual variables might modify the effects of therapy. In “effect modeling,” prediction models are developed directly on clinical trial data, including terms for treatment and treatment effect interactions. These more flexible approaches may better uncover individualized treatment effects, but are also prone to overfitting when dimensionality is high, power is low, and there is limited prior knowledge about effect modifiers.
  Citation: Clinical Trials
  PubDate: 2023-05-06T09:20:51Z
  DOI: 10.1177/17407745231171897
Using the Delphi process to determine the minimum clinically important
effect size for the Balanced-2 randomised controlled trial
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  Authors: Carolyn Deng, David Sidebotham
  Abstract: Clinical Trials, Ahead of Print.
  Background:The sample size calculation is an important step in designing randomised controlled trials. For a trial comparing a control and an intervention group, where the outcome is binary, the sample size calculation requires choosing values for the anticipated event rates in both the control and intervention groups (the effect size), and the error rates. The Difference ELicitation in TriAls guidance recommends that the effect size should be both realistic, and clinically important to stakeholder groups. Overestimating the effect size leads to sample sizes that are too small to reliably detect the true population effect size, which in turn results in low achieved power. In this study, we use the Delphi approach to gain consensus on what the minimum clinically important effect size is for Balanced-2, a randomised controlled trial comparing processed electroencephalogram-guided ‘light’ to ‘deep’ general anaesthesia on the incidence of postoperative delirium in older adults undergoing major surgery.Methods:Delphi rounds were conducted using electronic surveys. Surveys were administered to two stakeholder groups: specialist anaesthetists from a general adult department in Auckland City Hospital, New Zealand (Group 1), and specialist anaesthetists with expertise in clinical research, identified from the Australian and New Zealand College of Anaesthetist’s Clinical Trials Network (Group 2). A total of 187 anaesthetists were invited to participate (81 from Group 1 and 106 from Group 2). Results from each Delphi round were summarised and presented in subsequent rounds until consensus was reached (>70% agreement).Results:The overall response rate for the first Delphi survey was 47% (88/187). The median minimum clinically important effect size was 5.0% (interquartile range: 5.0–10.0) for both stakeholder groups. The overall response rate for the second Delphi survey was 51% (95/187). Consensus was reached after the second round, as 74% of respondents in Group 1 and 82% of respondents in Group 2 agreed with the median effect size. The combined minimum clinically important effect size across both groups was 5.0% (interquartile range: 3.0–6.5).Conclusions:This study demonstrates that surveying stakeholder groups using a Delphi process is a simple way of defining a minimum clinically important effect size, which aids the sample size calculation and determines whether a randomised study is feasible.
  Citation: Clinical Trials
  PubDate: 2023-05-05T10:10:36Z
  DOI: 10.1177/17407745231173058
Success of blinding a procedural intervention in a randomised controlled
trial in preterm infants receiving respiratory support
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  Authors: Elizabeth Reid, Omar F Kamlin, Francesca Orsini, Antonio G De Paoli, Howard W Clark, Roger F Soll, John B Carlin, Peter G Davis, Peter A Dargaville
  Abstract: Clinical Trials, Ahead of Print.
  Background:Blinding of treatment allocation from treating clinicians in neonatal randomised controlled trials can minimise performance bias, but its effectiveness is rarely assessed.Methods:To examine the effectiveness of blinding a procedural intervention from treating clinicians in a multicentre randomised controlled trial of minimally invasive surfactant therapy versus sham treatment in preterm infants of gestation 25–28 weeks with respiratory distress syndrome. The intervention (minimally invasive surfactant therapy or sham) was performed behind a screen within the first 6 h of life by a ‘study team’ uninvolved in clinical care including decision-making. Procedure duration and the study team’s words and actions during the sham treatment mimicked those of the minimally invasive surfactant therapy procedure. Post-intervention, three clinicians completed a questionnaire regarding perceived group allocation, with the responses matched against actual intervention and categorised as correct, incorrect, or unsure. Success of blinding was calculated using validated blinding indices applied to the data overall (James index, successful blinding defined as> 0.50), or to the two treatment allocation groups (Bang index, successful blinding: −0.30 to 0.30). Blinding success was measured within staff role, and the associations between blinding success and procedural duration and oxygenation improvement post-procedure were estimated.Results:From 1345 questionnaires in relation to a procedural intervention in 485 participants, responses were categorised as correct in 441 (33%), incorrect in 142 (11%), and unsure in 762 (57%), with similar proportions for each of the response categories in the two treatment arms. The James index indicated successful blinding overall 0.67 (95% confidence interval (CI) 0.65–0.70). The Bang index was 0.28 (95% CI 0.23–0.32) in the minimally invasive surfactant therapy group and 0.17 (95% CI 0.12–0.21) in the sham arm. Neonatologists more frequently guessed the correct intervention (47%) than bedside nurses (36%), neonatal trainees (31%), and other nurses (24%). For the minimally invasive surfactant therapy intervention, the Bang index was linearly related to procedural duration and oxygenation improvement post-procedure. No evidence of such relationships was seen in the sham arm.Conclusion:Blinding of a procedural intervention from clinicians is both achievable and measurable in neonatal randomised controlled trials.
  Citation: Clinical Trials
  PubDate: 2023-05-05T10:07:16Z
  DOI: 10.1177/17407745231171647
Experiences with recruitment and retention of adolescents and emerging
adults in a weight loss intervention trial
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  Authors: Gilbert Horst, Hailey Miller, Anna Peeler, Jeanne Charleston, Thomas Dell, Stephen P Juraschek, Tammy M Brady
  Abstract: Clinical Trials, Ahead of Print.
  Background/Aims:Efficient and effective participant recruitment is key for successful clinical research. Adolescent and emerging adult recruitment into clinical trials can be particularly challenging, especially when targeting underrepresented groups. This study aimed to determine the most successful recruitment strategies from those employed during a pediatric trial testing the efficacy of a behavioral intervention on adiposity and cardiovascular disease risk.Methods:We determined the effectiveness, cost, and diversity of the final research population by each recruitment method utilized in the EMPower trial, a randomized clinical trial designed to test the effect of a technology-delivered behavioral Healthy Lifestyle intervention on adiposity, blood pressure, and left ventricular mass among adolescents and emerging adults with overweight or obesity. Effectiveness was determined by respondent yield (RY; number of respondents/number contacted), scheduled yield (SY; number scheduled for a baseline visit/number of respondents), enrollment yield (EY; number enrolled/number of respondents), and retention (number completed/number enrolled). Cost-effectiveness of each recruitment method was calculated and demographics of participants recruited via each method was determined.Results:A minimum of 109,314 adolescents and emerging adults were contacted by at least one recruitment method (clinic, web-based, postal mailing, electronic medical record (EMR) messaging) leading to 429 respondents. The most successful strategies in terms of RY were clinic-based recruitment (n = 47, 61% RY), community web-postings (n = 109, 5.33% RY), and EMR messaging (n = 163, 0.99% RY); however, website, postal mailings, and EMR recruitment led to more successful SY and EY. Postal mailings were the most costly strategy to employ (US$3261/completed participant) with EMR messaging the second most costly (US$69/completed participant). Community web-postings were free of charge. Clinic-based recruitment did not add additional costs, per se, but did require a substantial amount of personnel time (63.6 h/completed participant). Final cohort diversity primarily came from postal mailings (57% Black) and EMR messages (50% female).Conclusion:Electronic medical record messaging and web-based recruitment were highly successful and cost-effective strategies in a pediatric clinical trial targeting adolescents and emerging adults, but was less successful in recruiting a diverse cohort. Clinic recruitment and postal mailings, despite being costly and time-consuming, were the strategies that enrolled a greater proportion of underrepresented groups. While online forms of trial recruitment are growing in popularity, clinic-based recruitment and non-web-based strategies may be required to ensure participant diversity and representation.
  Citation: Clinical Trials
  PubDate: 2023-04-28T05:08:01Z
  DOI: 10.1177/17407745231167090
Controlling the false-discovery rate when identifying the subgroup
benefiting from treatment
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  Authors: Patrick M Schnell
  Abstract: Clinical Trials, Ahead of Print.
  One common goal of subgroup analyses is to determine which (if any) types of patients—sets of patients sharing a vector of baseline covariates—benefit from a particular treatment. Many approaches involve testing, implicitly or explicitly, hypotheses about many patient types which are nonexchangeable. Methods of controlling family-wise Type I error rate inflation in such approaches are available. Such methods are designed to control the rate of erroneously declaring at least one type of patient as benefiting and are, therefore, quite conservative. We present a method for instead controlling a weighted false discovery rate in the sense of controlling the expected proportion of patient types declared benefiting, weighted by their population prevalence, which do not in fact benefit from treatment. Such population-weighted false discovery rate control is analogous to maintaining the positive predictive value of a diagnostic test for expected benefit. We minimize power loss by using a resampling approach that accounts for correlation among test statistics corresponding to similar patient types. Simulation studies demonstrate successful control of the weighted false discovery rate by the proposed method, as well as anti-conservativeness in the absence of multiplicity corrections and conservativeness by methods controlling the false discovery rate without accounting for dependent test statistics or controlling the family-wise error rate. An analysis of a clinical trial of an Alzheimer’s disease treatment illustrates the approach on real data. Resampling-based methods allow weighted false discovery rate control without unnecessarily sacrificing power when treatment effect estimates are correlated among patient types, and admit useful interpretations in terms of bounding sets and positive predictive value.
  Citation: Clinical Trials
  PubDate: 2023-04-26T05:30:08Z
  DOI: 10.1177/17407745231169300
Finding the (biomarker-defined) subgroup of patients who benefit from a
novel therapy: No time for a game of hide and seek
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  Authors: Lisa Meier McShane, Mark D Rothmann, Thomas R Fleming
  Abstract: Clinical Trials, Ahead of Print.
  An important element of precision medicine is the ability to identify, for a specific therapy, those patients for whom benefits of that therapy meaningfully exceed the risks. To achieve this goal, treatment effect usually is examined across subgroups defined by a variety of factors, including demographic, clinical, or pathologic characteristics or by molecular attributes of patients or their disease. Frequently such subgroups are defined by the measurement of biomarkers. Even though such examination is necessary when pursuing this goal, the evaluation of treatment effect across a variety of subgroups is statistically fraught due to both the danger of inflated false-positive error rate from multiple testing and the inherent insensitivity to how treatment effects differ across subgroups.Pre-specification of subgroup analyses with appropriate control of false-positive (i.e. type I) error is recommended when possible. However, when subgroups are specified by biomarkers, which could be measured by different assays and might lack established interpretation criteria, such as cut-offs, it might not be possible to fully specify those subgroups at the time a new therapy is ready for definitive evaluation in a Phase 3 trial. In these situations, further refinement and evaluation of treatment effect in biomarker-defined subgroups might have to take place within the trial. A common scenario is that evidence suggests that treatment effect is a monotone function of a biomarker value, but optimal cut-offs for therapy decisions are not known. In this setting, hierarchical testing strategies are widely used, where testing is first conducted in a particular biomarker-positive subgroup and then is conducted in the expanded pool of biomarker-positive and biomarker-negative patients, with control for multiple testing. A serious limitation of this approach is the logical inconsistency of excluding the biomarker-negatives when evaluating effects in the biomarker-positives, yet allowing the biomarker-positives to drive the assessment of whether a conclusion of benefit could be extrapolated to the biomarker-negative subgroup.Examples from oncology and cardiology are described to illustrate the challenges and pitfalls. Recommendations are provided for statistically valid and logically consistent subgroup testing in these scenarios as alternatives to reliance on hierarchical testing alone, and approaches for exploratory assessment of continuous biomarkers as treatment effect modifiers are discussed.
  Citation: Clinical Trials
  PubDate: 2023-04-25T05:24:54Z
  DOI: 10.1177/17407745231169692
Dynamic data-enabled stratified sampling for trial invitations with
application in NHS-Galleri
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  Authors: Adam R Brentnall, Chris Mathews, Sandy Beare, Jennifer Ching, Michelle Sleeth, Peter Sasieni
  Abstract: Clinical Trials, Ahead of Print.
  Background:Participants of health research studies such as cancer screening trials usually have better health than the target population. Data-enabled recruitment strategies might be used to help minimise healthy volunteer effects on study power and improve equity.Methods:A computer algorithm was developed to help target trial invitations. It assumes participants are recruited from distinct sites (such as different physical locations or periods in time) that are served by clusters (such as general practitioners in England, or geographical areas), and the population may be split into defined groups (such as age and sex bands). The problem is to decide the number of people to invite from each group, such that all recruitment slots are filled, healthy volunteer effects are accounted for, and equity is achieved through representation in sufficient numbers of all major societal and ethnic groups. A linear programme was formulated for this problem.Results:The optimisation problem was solved dynamically for invitations to the NHS-Galleri trial (ISRCTN91431511). This multi-cancer screening trial aimed to recruit 140,000 participants from areas in England over 10 months. Public data sources were used for objective function weights, and constraints. Invitations were sent by sampling according to lists generated by the algorithm. To help achieve equity the algorithm tilts the invitation sampling distribution towards groups that are less likely to join. To mitigate healthy volunteer effects, it requires a minimum expected event rate of the primary outcome in the trial.Conclusion:Our invitation algorithm is a novel data-enabled approach to recruitment that is designed to address healthy volunteer effects and inequity in health research studies. It could be adapted for use in other trials or research studies.
  Citation: Clinical Trials
  PubDate: 2023-04-25T05:22:33Z
  DOI: 10.1177/17407745231167369
Designing a childhood obesity preventive intervention using the multiphase
optimization strategy: The Healthy Bodies Project
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  Authors: Lori A Francis, Robert L Nix, Rhonda BeLue, Kathleen L Keller, Kari C Kugler, Brandi Y Rollins, Jennifer S Savage
  Abstract: Clinical Trials, Ahead of Print.
  Background/Aims:Preventing the development of childhood obesity requires multilevel, multicomponent, comprehensive approaches. Study designs often do not allow for systematic evaluation of the efficacy of individual intervention components before the intervention is fully tested. As such, childhood obesity prevention programs may contain a mix of effective and ineffective components. This article describes the design and rationale of a childhood obesity preventive intervention developed using the multiphase optimization strategy, an engineering-inspired framework for optimizing behavioral interventions. Using a series of randomized experiments, the objective of the study was to systematically test, select, and refine candidate components to build an optimized childhood obesity preventive intervention to be evaluated in a subsequent randomized controlled trial.Methods:A 24 full factorial design was used to test the individual and combined effects of four candidate intervention components intended to reduce the risk for childhood obesity. These components were designed with a focus on (a) improving children’s healthy eating behaviors and nutrition knowledge, (b) increasing physical activity and reducing sedentary activity in the childcare setting, (c) improving children’s behavioral self-regulation, and (d) providing parental web-based education to address child target outcomes. The components were tested with approximately 1400 preschool children, ages 3–5 years in center-based childcare programs in Pennsylvania, the majority of which served predominantly Head-Start eligible households. Primary child outcomes included healthy eating knowledge, physical and sedentary activity, and behavioral self-regulation. Secondary outcomes included children’s body mass index and appetitive traits related to appetite regulation.Results:Four intervention components were developed, including three classroom curricula designed to increase preschool children’s nutrition knowledge, physical activity, and behavioral, emotional, and eating regulation. A web-based parent education component included 18 lessons designed to improve parenting practices and home environments that would bolster the effects of the classroom curricula. A plan for analyzing the specific contribution of each component to a larger intervention was developed and is described. The efficacy of the four components can be evaluated to determine the extent to which they, individually and in combination, produce detectable changes in childhood obesity risk factors. The resulting optimized intervention should later be evaluated in a randomized controlled trial, which may provide new information on promising targets for obesity prevention in young children.Conclusion:This research project highlights the ways in which an innovative approach to the design and initial evaluation of preventive interventions may increase the likelihood of long-term success. The lessons from this research project have implications for childhood obesity research as well as other preventive interventions that include multiple components, each targeting unique contributors to a multifaceted problem.
  Citation: Clinical Trials
  PubDate: 2023-04-20T05:06:01Z
  DOI: 10.1177/17407745231167115
Definition and rationale for placebo composition: Cross-sectional analysis
of randomized trials and protocols published in high-impact medical
journals
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  Authors: Kyungwan Hong, Anisa Rowhani-Farid, Peter Doshi
  Abstract: Clinical Trials, Ahead of Print.
  Background/Aims:Inadequate description of trial interventions in publications has been repeatedly reported, a problem that extends to the description of placebo controls. Without describing placebo contents, it cannot be assumed that a placebo is inert. Pharmacologically active placebos complicate accurate estimation and interpretation of efficacy and safety data. In this study, we sought to assess whether placebo contents are described in study protocols and publications of trials published in high-impact medical journals.Methods:We identified all placebo-controlled randomized clinical trials (RCTs) published in 2016 in Annals of Internal Medicine, The BMJ, the Journal of the American Medical Association (JAMA), The Lancet, and the New England Journal of Medicine (NEJM). We included all trials with publicly available study protocols. From journal publications and associated study protocols, we searched and recorded: description of placebo contents; the amount of each placebo ingredient; and investigators’ stated rationale for selection of placebo ingredients.Results:We included 113 placebo-controlled RCTs. Of the 113 trials, placebo content was described in 22 (19.5%) journal publications and 51 (45.1%) study protocols. The amount of each placebo ingredient was described in 15 (13.3%) journal publications and 47 (41.6%) study protocols. None of the journal publications explained the rationale for the choice of placebo ingredients, whereas a rationale was provided in 4 (3.5%) study protocols. The stated rationales were to ensure the placebo was visually indistinguishable from the experimental intervention (N = 3) and ensure comparability with a previous study (N = 1).Conclusion:There is no accessible record of the composition of placebos for approximately half of high-impact RCTs, even with access to study protocols. This impedes reproducibility and raises unanswerable questions about what effects—beneficial or harmful—the placebo may have had on trial participants, potentially confounding an accurate assessment of the experimental intervention’s safety and efficacy. Considering that study protocols are unabridged, detailed documents describing the trial design and methodology, the fact that less than half of the study protocols described the placebo contents raises concerns about clinical trial transparency. To improve the reproducibility and potential of placebo-controlled RCTs to provide reliable evidence on the efficacy and safety profile of drugs and other experimental interventions, more detail regarding placebo contents must be included in trial documents.
  Citation: Clinical Trials
  PubDate: 2023-04-13T05:51:35Z
  DOI: 10.1177/17407745231167756
Lessons learned from conducting the first cancer care delivery trial in
the Alliance for Clinical Trials in Oncology (Alliance A191402CD)
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  Authors: Joel E Pacyna, Amylou C Dueck, George J Chang, Selina Chow, Electra D Paskett, Simon Kim, Jon C Tilburt
  Abstract: Clinical Trials, Ahead of Print.
  Introduction:Testing healthcare delivery interventions in rigorous clinical trials is a critical step in improving patient care, but conducting multisite randomized clinical trials to test the effect of care delivery interventions has unique challenges and requires foresight and planning.Methods:We conducted the first care delivery trial (A191402CD) in the Alliance for Clinical Trials in Oncology, a National Cancer Institute Community Oncology Research Program research base, which tested the effectiveness of two different decision aids for supporting shared decision-making about prostate cancer treatment. Our experience illustrates the kind of challenges that confront care delivery researchers as they seek to test interventions to improve the experiences of patients.Results:Lessons learned include the following: cluster-randomized designs introduce complexity; workflow disruption can discourage site participation; evidence-based methods may not always be sufficient.Conclusion:We conclude with the following recommendations: assessing feasibility requires special rigor; relationships and interpersonal dynamics must be leveraged. Our experiences may inform future care delivery research.
  Citation: Clinical Trials
  PubDate: 2023-04-13T05:41:28Z
  DOI: 10.1177/17407745231167123
A model for conducting clinical trials via telemedicine
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  Authors: Gary C Doolittle, Ashley Spaulding, Thomas M Irwin, Barbara Adkins, Adrian Caracioni
  Abstract: Clinical Trials, Ahead of Print.
  
  Citation: Clinical Trials
  PubDate: 2023-04-12T10:44:20Z
  DOI: 10.1177/17407745231167145
Subgroup analyses and pre-specification
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  Authors: Ellis F Unger
  Abstract: Clinical Trials, Ahead of Print.
  Large clinical trials provide the opportunity to assess treatment effects in subgroups of patients, based on baseline demographic and disease-related factors, and there is always great interest in these analyses. Generally, the term “pre-specification” has major ramifications for clinical trials, particularly for adequate and well-controlled trials that are designed for formal hypothesis testing. Pre-specification is the “holy grail” of modern trials, as choosing analytical approaches with data in-hand will inflate the type I error rate. But “pre-specification” often has a different meaning with respect to subgroup analyses.
  Citation: Clinical Trials
  PubDate: 2023-03-30T05:03:11Z
  DOI: 10.1177/17407745231160540