Abstract WP25: A Novel Adaptive Trial Design Increases the Power of Endovascular Stroke Studies

Stroke ◽  
2016 ◽  
Vol 47 (suppl_1) ◽  
Author(s):  
Maarten Lansberg ◽  
Ninad Bhat ◽  
Joseph P Broderick ◽  
Yuko Y Palesch ◽  
Philip W Lavori ◽  
...  
Keyword(s):  
Treatment Effect ◽  
Interim Analysis ◽  
Trial Design ◽  
Adaptive Design ◽  
Type I ◽  
Vessel Occlusion ◽  
Patient Subgroup ◽  
Traditional Design ◽  
Subgroup Selection ◽  
Adaptive Trial

Introduction: It is difficult to choose trial enrollment criteria that will yield a robust treatment effect. To address this problem, we developed a novel trial design that restricts enrollment criteria to the patient subgroup most likely to show benefit, if an interim analysis indicates futility in the overall sample. Future recruitment, and the population in which the primary hypothesis is tested, is limited to the selected subgroup. Hypothesis: A design with adaptive subgroup selection increases the power of endovascular stroke studies. Methods: We ran simulations to compare the power of the adaptive design with that of a traditional design. Trial parameters were: type I error 0.025, type II error 0.1, analysis after 450, 675 and 900 patients (interim and final analyses in IMS III). Outcome data were based on 90 day mRS scores observed in IMS III among patients with a vessel occlusion on baseline CTA (n=289). Subgroups were defined a priori according to vessel occlusion (ICA ± distal occlusion vs M1 vs M2-4), onset-to-randomization time (early vs late), and treatment allocation (IA+IV vs IV alone). The treatment effect in the overall cohort was a mean mRS improvement of 0.15 (2.41 for IV+IA vs 2.56 for IV alone; SD 1.45). The subgroup treatment effects were: early ICA = 0.54, late ICA = 0.60, early M1 = 0.33, late M1 = 0.07, early M2-4 = -0.66, and late M2-4 = -0.35. Results: The traditional design showed a treatment benefit in 31% of simulations. The adaptive design showed benefit in 91%, failed to show benefit after enrollment of the maximum sample in 1%, and stopped early for futility in 8% of simulations. The adaptive trial stopped early for benefit in 84% of simulations. Due to early stopping, the mean number of patients randomized is 590±140 with the adaptive design vs 900 with a traditional design. Of the adaptive trial simulations that showed benefit, 91% occur after subgroup selection. The subgroup selected most often (31% of all simulations) includes early and late ICA patients. Conclusions: A trial with adaptive subgroup selection can efficiently test the effect of endovascular stroke treatment. Simulations suggest that with this design, IMS III would have 91% power and would typically stop early after interim analysis shows benefit in a patient subgroup.

scholarly journals The FDA adaptive trial design guidance in a nutshell - A review in Q&A format for decision makers

2016 ◽  
Author(s):  
Klaus Gottlieb
Keyword(s):  
Trial Design ◽  
Adaptive Design ◽  
Decision Makers ◽  
Development Programs ◽  
Fda Guidance ◽  
Regulatory Affairs ◽  
Frequent Use ◽  
Adaptive Trial Design ◽  
Adaptive Trial ◽  
Design Guidance

The FDA adaptive trial design guidance (1) is a thoughtful but lengthy document that explains on 50 pages wide-ranging and important topics “such as ... what aspects of adaptive design trials (i.e., clinical, statistical, regulatory) call for special consideration, ... when to interact with FDA while planning and conducting adaptive design studies, ... what information to include in the adaptive design for FDA review, and ... issues to consider in the evaluation of a completed adaptive design study.” [20-24]. The advice in the guidance is often misinterpreted, misquoted or ignored. This is unfortunate because an appropriate use of adaptive designs could increase the chances of success in drug development programs. Decision makers rely on the advice of regulatory affairs professionals and statisticians to interpret the guidance. Unfortunately, many clinical trial statisticians and regulatory professionals only have a rudimentary understanding of the guidance, presumably because the document is somewhat inscrutable for both audiences, too ‘regulatory’ for statisticians, too ‘statistical’ for regulatory people. This digest was therefore written with three goals in mind: 1) Make the content of the guidance more accessible through a question & answer format, 2) shorten the content from 50 to 10 pages by excerpting the most important dictums, and 3) keep fidelity to the original guidance by frequent use of direct quotes with reference to the respective lines in the original FDA guidance where the quote can be found in square brackets.

scholarly journals The FDA adaptive trial design guidance in a nutshell - A review in Q&A format for decision makers

2016 ◽  
Author(s):  
Klaus Gottlieb
Keyword(s):  
Trial Design ◽  
Adaptive Design ◽  
Decision Makers ◽  
Development Programs ◽  
Fda Guidance ◽  
Regulatory Affairs ◽  
Frequent Use ◽  
Adaptive Trial Design ◽  
Adaptive Trial ◽  
Design Guidance

The FDA adaptive trial design guidance (1) is a thoughtful but lengthy document that explains on 50 pages wide-ranging and important topics “such as ... what aspects of adaptive design trials (i.e., clinical, statistical, regulatory) call for special consideration, ... when to interact with FDA while planning and conducting adaptive design studies, ... what information to include in the adaptive design for FDA review, and ... issues to consider in the evaluation of a completed adaptive design study.” [20-24]. The advice in the guidance is often misinterpreted, misquoted or ignored. This is unfortunate because an appropriate use of adaptive designs could increase the chances of success in drug development programs. Decision makers rely on the advice of regulatory affairs professionals and statisticians to interpret the guidance. Unfortunately, many clinical trial statisticians and regulatory professionals only have a rudimentary understanding of the guidance, presumably because the document is somewhat inscrutable for both audiences, too ‘regulatory’ for statisticians, too ‘statistical’ for regulatory people. This digest was therefore written with three goals in mind: 1) Make the content of the guidance more accessible through a question & answer format, 2) shorten the content from 50 to 10 pages by excerpting the most important dictums, and 3) keep fidelity to the original guidance by frequent use of direct quotes with reference to the respective lines in the original FDA guidance where the quote can be found in square brackets.

scholarly journals Enhancing Signal Detection in Clinical Trials

2009 ◽  
Vol 24 (S1) ◽  
pp. 1-1
Author(s):  
A. Leon
Keyword(s):  
Clinical Trial ◽  
Signal Detection ◽  
Treatment Effect ◽  
Trial Design ◽  
Statistical Power ◽  
Type I Error ◽  
False Negative ◽  
Clinical Trial Design ◽  
Type I ◽  
Negative Results

Dr. Leon will present the biostatistical considerations that contribute to a clinical trial design and the strategies to enhance signal detection. These include minimizing bias in the estimate of treatment effect while maintaining a nominal level of type I error (i.e., false positive results) and maintaining sufficient statistical power (i.e. reducing the likelihood of false negative results). Particular attention will be paid to reducing the problems of attrition and the hazards of multiplicity. Methods to examine moderators of the treatment effect will also be explored. Examples from psychopharmacologic and psychotherapy trials for the treatment of depression and panic disorder will be provided to illustrate these issues. Following the didactic session, the participants will be encouraged to bring forth their own questions regarding clinical trial design for a 45-minute interactive discussion with the presenters. The objectives of the workshop are to improve the participants’ understanding of the goals of clinical trial design and methods to achieve those goals in order to improve their own research techniques, grantsmanship, and abilities to more accurately judge the results of studies presented in the literature.

Reinventing the Wheel in Clinical Trial Design

2009 ◽  
Vol 24 (S1) ◽  
pp. 1-1
Author(s):  
L. Davis ◽  
A. Leon
Keyword(s):  
Clinical Trial ◽  
Treatment Effect ◽  
Trial Design ◽  
Statistical Power ◽  
False Negative ◽  
Clinical Trial Design ◽  
Essential Elements ◽  
Type I ◽  
Institute Of Medicine ◽  
Trial Methodology

Recent publications by the Institute of Medicine have unearthed several fundamental flaws in clinical trial methodology that, if corrected by the next generation of clinical investigators, can transform the field of mental health intervention research. Using a clinician-friendly approach, this workshop will succinctly review the essential elements of optimal design and implementation of a randomized controlled clinical study and the strategies to enhance signal detection. These include minimizing bias in the estimate of treatment effect while maintaining a nominal level of type I error (i.e., false positive results) and maintaining sufficient statistical power (i.e. reducing the likelihood of false negative results). Particular attention will be paid to reducing the problems of attrition and the hazards of multiplicity. Methods to examine moderators of the treatment effect will also be explored. Examples from psychopharmacologic, psychotherapy, and vocational rehabilitation trials for the treatment of posttraumatic stress disorder, depression, and panic disorder will be provided to illustrate these issues. Techniques to reduce the study's costs, risks, and participant burden will be described. Following the didactic session, the participants are encouraged to bring forth their own questions regarding clinical trial design for a 45-minute interactive discussion with the presenters. The objectives of the workshop are to improve the participants’ understanding of the goals of clinical trial design and methods to achieve those goals in order to improve their own research techniques, grantsmanship, and abilities to more accurately judge the results of studies presented in the literature.

scholarly journals A review of available software for adaptive clinical trial design

2020 ◽  
Vol 17 (3) ◽  
pp. 323-331
Author(s):  
Michael John Grayling ◽  
Graham Mark Wheeler
Keyword(s):  
Trial Design ◽  
Adaptive Design ◽  
Clinical Trial Design ◽  
Adaptive Designs ◽  
Group Sequential Design ◽  
Group Sequential ◽  
Adaptive Trial Design ◽  
Code Distribution ◽  
User Friendly ◽  
Adaptive Trial

Background/aims: The increasing cost of the drug development process has seen interest in the use of adaptive trial designs grow substantially. Accordingly, much research has been conducted to identify barriers to increasing the use of adaptive designs in practice. Several articles have argued that the availability of user-friendly software will be an important step in making adaptive designs easier to implement. Therefore, we present a review of the current state of software availability for adaptive trial design. Methods: We review articles from 31 journals published in 2013–2017 that relate to methodology for adaptive trials to assess how often code and software for implementing novel adaptive designs is made available at the time of publication. We contrast our findings against these journals’ policies on code distribution. We also search popular code repositories, such as Comprehensive R Archive Network and GitHub, to identify further existing user-contributed software for adaptive designs. From this, we are able to direct interested parties toward solutions for their problem of interest. Results: Only 30% of included articles made their code available in some form. In many instances, articles published in journals that had mandatory requirements on code provision still did not make code available. There are several areas in which available software is currently limited or saturated. In particular, many packages are available to address group sequential design, but comparatively little code is present in the public domain to determine biomarker-guided adaptive designs. Conclusions: There is much room for improvement in the provision of software alongside adaptive design publications. In addition, while progress has been made, well-established software for various types of trial adaptation remains sparsely available.

Abstract 269: The Design of an Adaptive Clinical Trial - The Extracorporeal Cardiopulmonary Resuscitation for Refractory Out of Hospital Cardiac Arrest (EROCA) Project

Circulation ◽  
2020 ◽  
Vol 142 (Suppl_4) ◽  
Author(s):  
Juliana Tolles ◽  
Kelley Kidwell ◽  
Kristine Broglio ◽  
Todd Graves ◽  
William J Meurer ◽  
...  
Keyword(s):  
Clinical Trial ◽  
Cardiac Arrest ◽  
Cardiopulmonary Resuscitation ◽  
Trial Design ◽  
Adaptive Design ◽  
Therapeutic Window ◽  
Extracorporeal Cardiopulmonary Resuscitation ◽  
Design Modification ◽  
Hospital Cardiac Arrest ◽  
Adaptive Trial

Introduction: Extracorporeal cardiopulmonary resuscitation (ECPR) for out-of-hospital cardiac arrest (OHCA) is promising but unproven. We developed a concept for a randomized Bayesian adaptive clinical trial to define the interval after arrest during which patients derive benefit. Hypotheses: We hypothesized that the simulated design characteristics of the adaptive design would efficiently confirm or refute the existence of a therapeutic window for ECPR in a future trial. Methods: Through iterative simulation and design modification, we developed a Bayesian adaptive trial of ECPR for adults with OHCA. The goals of the trial design were to address the uncertainty surrounding the OHCA-to-ECPR interval within which clinical benefit might be preserved and the difference in prognosis between patients with non-shockable vs shockable rhythms. The treatment effect was defined as the mean 90-day utility-weighted Modified Rankin Scale (uwmRS) for each rhythm subgroup and estimated CA-to-ECPR interval. The trial adaptively lengthens or contracts the estimated CA-to-ECPR eligibility window depending on the probability of benefit from ECPR. We simulated trial performance under six potential ECPR efficacy scenarios. We compared the adaptive design to a fixed design. Results: The trial had 91-100% power to detect a benefit from ECPR for non-shockable rhythms and 69-98% power for shockable rhythms under the scenarios simulated (figure). The design had a high probability of identifying the maximum CA-to-ECPR interval within which ECPR produces a clinically significant benefit of 0.2 on the uwMRS. In null scenarios, the 97-99% of the simulated trials ended early declaring futility. Conclusions: The adaptive trial design helps to ensure the patient population most likely to benefit from treatment—as defined by rhythm subgroup and estimated CA-to-ECPR interval—are enrolled. The design also promotes early termination of the trial if continuation is likely to be futile.

scholarly journals Development of a Bayesian response-adaptive trial design for the Dexamethasone for Excessive Menstruation study

2015 ◽  
Vol 26 (6) ◽  
pp. 2681-2699
Author(s):  
Christian Holm Hansen ◽  
Pamela Warner ◽  
Richard A Parker ◽  
Brian R Walker ◽  
Hilary OD Critchley ◽  
...  
Keyword(s):  
Dose Response ◽  
Trial Design ◽  
Statistical Power ◽  
Design Feature ◽  
Fractional Factorial ◽  
Dose Finding ◽  
Type I ◽  
Efficient Design ◽  
Adaptive Trial Design ◽  
Adaptive Trial

It is often unclear what specific adaptive trial design features lead to an efficient design which is also feasible to implement. This article describes the preparatory simulation study for a Bayesian response-adaptive dose-finding trial design. Dexamethasone for Excessive Menstruation aims to assess the efficacy of Dexamethasone in reducing excessive menstrual bleeding and to determine the best dose for further study. To maximise learning about the dose response, patients receive placebo or an active dose with randomisation probabilities adapting based on evidence from patients already recruited. The dose-response relationship is estimated using a flexible Bayesian Normal Dynamic Linear Model. Several competing design options were considered including: number of doses, proportion assigned to placebo, adaptation criterion, and number and timing of adaptations. We performed a fractional factorial study using SAS software to simulate virtual trial data for candidate adaptive designs under a variety of scenarios and to invoke WinBUGS for Bayesian model estimation. We analysed the simulated trial results using Normal linear models to estimate the effects of each design feature on empirical type I error and statistical power. Our readily-implemented approach using widely available statistical software identified a final design which performed robustly across a range of potential trial scenarios.

scholarly journals Testing for treatment effect in covariate-adaptive randomized trials with generalized linear models and omitted covariates

2021 ◽  
pp. 096228022110082
Author(s):  
Yang Li ◽  
Wei Ma ◽  
Yichen Qin ◽  
Feifang Hu
Keyword(s):  
Generalized Linear Models ◽  
Treatment Effect ◽  
Linear Models ◽  
Type I Error ◽  
Type I ◽  
Test Statistic ◽  
Asymptotic Results ◽  
Adaptive Randomization ◽  
Adjustment Method ◽  
Inflated Type

Concerns have been expressed over the validity of statistical inference under covariate-adaptive randomization despite the extensive use in clinical trials. In the literature, the inferential properties under covariate-adaptive randomization have been mainly studied for continuous responses; in particular, it is well known that the usual two-sample t-test for treatment effect is typically conservative. This phenomenon of invalid tests has also been found for generalized linear models without adjusting for the covariates and are sometimes more worrisome due to inflated Type I error. The purpose of this study is to examine the unadjusted test for treatment effect under generalized linear models and covariate-adaptive randomization. For a large class of covariate-adaptive randomization methods, we obtain the asymptotic distribution of the test statistic under the null hypothesis and derive the conditions under which the test is conservative, valid, or anti-conservative. Several commonly used generalized linear models, such as logistic regression and Poisson regression, are discussed in detail. An adjustment method is also proposed to achieve a valid size based on the asymptotic results. Numerical studies confirm the theoretical findings and demonstrate the effectiveness of the proposed adjustment method.

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