Information‐based sample size re‐estimation in group sequential design for longitudinal trials

Zhou, Jing; Adewale, Adeniyi J.; Shentu, Yue; Liu, Jiajun; Anderson, Keaven M.

doi:10.1002/sim.6192

Cited by 3 publications

(2 citation statements)

References 21 publications

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“…In this review paper, we will focus on confirmatory adaptive designs in the sense of . Many other important developments exist, such as blinded sample size reassessment , Bayesian adaptive designs , response‐adaptive randomization , and type I error rate control by exact calculation or simulations of critical boundaries for pre‐specified set of adaptation rules. However, whether using simulations is sufficient ‘to prove’ type I error rate control without any doubts remains controversial .…”

Section: Introductionmentioning

confidence: 99%

Twenty‐five years of confirmatory adaptive designs: opportunities and pitfalls

Bauer

Bretz

Dragalin

et al. 2015

Statistics in Medicine

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'Multistage testing with adaptive designs' was the title of an article by Peter Bauer that appeared 1989 in the German journal Biometrie und Informatik in Medizin und Biologie. The journal does not exist anymore but the methodology found widespread interest in the scientific community over the past 25 years. The use of such multistage adaptive designs raised many controversial discussions from the beginning on, especially after the publication by Bauer and Köhne 1994 in Biometrics: Broad enthusiasm about potential applications of such designs faced critical positions regarding their statistical efficiency. Despite, or possibly because of, this controversy, the methodology and its areas of applications grew steadily over the years, with significant contributions from statisticians working in academia, industry and agencies around the world. In the meantime, such type of adaptive designs have become the subject of two major regulatory guidance documents in the US and Europe and the field is still evolving. Developments are particularly noteworthy in the most important applications of adaptive designs, including sample size reassessment, treatment selection procedures, and population enrichment designs. In this article, we summarize the developments over the past 25 years from different perspectives. We provide a historical overview of the early days, review the key methodological concepts and summarize regulatory and industry perspectives on such designs. Then, we illustrate the application of adaptive designs with three case studies, including unblinded sample size reassessment, adaptive treatment selection, and adaptive endpoint selection. We also discuss the availability of software for evaluating and performing such designs. We conclude with a critical review of how expectations from the beginning were fulfilled, and -if not -discuss potential reasons why this did not happen.

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Section: Introductionmentioning

confidence: 99%

Twenty‐five years of confirmatory adaptive designs: opportunities and pitfalls

Bauer

Bretz

Dragalin

et al. 2015

Statistics in Medicine

187

203

View full text Add to dashboard Cite

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“…At an interim stage of a clinical trial, a study population consists of completers, ie, subjects who complete the study or complete their target visit, and on‐going subjects, ie, who have not reached their target visit and only have data from earlier and intermediate visits. As discussed by many researchers, compared with utilizing data from completers only, making use of all available longitudinal information from both completers and on‐going subjects can lead to more efficient designs. Data from intermediate visits or earlier visits may well correlate with data from the long‐term target visit and provide important and valuable information for characterizing drug profile.…”

Section: Introductionmentioning

confidence: 99%

Predictive probability methods for interim monitoring in clinical trials with longitudinal outcomes

Zhou

Tang

Lang

et al. 2018

Statistics in Medicine

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In clinical research and development, interim monitoring is critical for better decision-making and minimizing the risk of exposing patients to possible ineffective therapies. For interim futility or efficacy monitoring, predictive probability methods are widely adopted in practice. Those methods have been well studied for univariate variables. However, for longitudinal studies, predictive probability methods using univariate information from only completers may not be most efficient, and data from on-going subjects can be utilized to improve efficiency. On the other hand, leveraging information from on-going subjects could allow an interim analysis to be potentially conducted once a sufficient number of subjects reach an earlier time point. For longitudinal outcomes, we derive closed-form formulas for predictive probabilities, including Bayesian predictive probability, predictive power, and conditional power and also give closed-form solutions for predictive probability of success in a future trial and the predictive probability of success of the best dose. When predictive probabilities are used for interim monitoring, we study their distributions and discuss their analytical cutoff values or stopping boundaries that have desired operating characteristics. We show that predictive probabilities utilizing all longitudinal information are more efficient for interim monitoring than that using information from completers only. To illustrate their practical application for longitudinal data, we analyze 2 real data examples from clinical trials.

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