Unbiased estimation for response adaptive clinical trials

Bowden, Jack; Trippa, Lorenzo

doi:10.1177/0962280215597716

Cited by 46 publications

(49 citation statements)

References 42 publications

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“…The FLGI design results in a larger underestimation of the time trend coefficient. This underestimation is consistent with that observed in Villar et al for reasons clarified in Bowden and Trippa . The power to detect a significant time trend for the FLGI is more than halved compared to CR.…”

Section: Adjusting the Model For A Time Trendsupporting

confidence: 91%

Response‐adaptive designs for binary responses: How to offer patient benefit while being robust to time trends?

Villar

Bowden

Wason

2017

Pharmaceutical Statistics

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Response‐adaptive randomisation (RAR) can considerably improve the chances of a successful treatment outcome for patients in a clinical trial by skewing the allocation probability towards better performing treatments as data accumulates. There is considerable interest in using RAR designs in drug development for rare diseases, where traditional designs are not either feasible or ethically questionable. In this paper, we discuss and address a major criticism levelled at RAR: namely, type I error inflation due to an unknown time trend over the course of the trial. The most common cause of this phenomenon is changes in the characteristics of recruited patients—referred to as patient drift. This is a realistic concern for clinical trials in rare diseases due to their lengthly accrual rate. We compute the type I error inflation as a function of the time trend magnitude to determine in which contexts the problem is most exacerbated. We then assess the ability of different correction methods to preserve type I error in these contexts and their performance in terms of other operating characteristics, including patient benefit and power. We make recommendations as to which correction methods are most suitable in the rare disease context for several RAR rules, differentiating between the 2‐armed and the multi‐armed case. We further propose a RAR design for multi‐armed clinical trials, which is computationally efficient and robust to several time trends considered.

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Section: Adjusting the Model For A Time Trendsupporting

confidence: 91%

Response‐adaptive designs for binary responses: How to offer patient benefit while being robust to time trends?

Villar

Bowden

Wason

2017

Pharmaceutical Statistics

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“…As compared to fixed equal randomization, the BRAR is more likely to terminate trials early and has been shown to lead to inflated type I error and biased treatment estimates. [5][6][7][8][9][10][11] Having more subjects assigned to the better-performing arm may improve the efficiency to identify the superior treatment arm, depending on the model used for the efficacy analysis and success rate of the two treatments. However, in the presence of a time-trend, the For subjects in the arm without specific trend pattern, a random sampling is conducted among all treatment arm subjects.…”

Section: Discussionmentioning

confidence: 99%

“…Altman et al studied the time-trend and suggested investigators to monitor the time-trend graphically [4].However, theresponse time-trend hasrarely been examinedin trial practice. The presence of a time-trend may bringbiasin treatment effect estimates, andinflate type I error [5][6][7][8][9][10][11][12].In order to better understandthe impact of time-trend, we incorporated a BRAR design into a previously completed randomized controlled phase 3 trial with a time-trend. The goal of this simulation study is to investigate drawbacks of using BRAR in the presence of time-trend, and to explore potential solutions to the problem.…”

Section: Introductionmentioning

confidence: 99%

Bayesian Response Adaptive Randomization in Clinical Trials With a Time-trend: Application to a Real Trial

Jiang

Zhao

Durkalski‐Mauldin

2020

Preprint

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Abstract Background In clinical trials with a large sample size, time-trend in response rates can affect the performance of Bayesian response adaptive randomization (BRAR). Methods To evaluate this impact, we utilize data from a previously completed randomized controlled trial that used a fixed 1:1 allocation. Subject response data from this study demonstrate a clear time-trend in the control group, but not in the treatment group. In this simulation study, we re-assign patients to treatment groups based on a BRAR algorithm, to examine the performance of BRAR as measured by the treatment effect estimation, the probability of early stopping, and the shift in adaptive allocation. Results Results from specific simulated study scenario show that in the presence of a time-trend, the timing of the first interim analysis is critical for the efficacy/futility decision making. Compared with fixed equal allocation, BRAR results in a higher probability of premature early stopping when time-trend effect exists. The magnitude of such impact varies among different BRAR algorithms. Conclusions Influential factors such as time trend, should be considered when planning the implementation of BRAR in large trials. Trial Registration: URL: https://www.clinicaltrials.gov. Unique identifier: NCT00235495. Registered on October 10, 2005.

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“…Several authors have compared frequentist and Bayesian approaches in trial designs . However, it is, in general, far from straightforward to evaluate Bayesian adaptive designs using frequentist criteria, particularly when the Bayesian design is the solution of a decision theoretic optimization. In this paper, we propose a general simulation‐based approach to evaluate Bayesian adaptive designs in terms of frequentist criteria.…”

Section: Introductionmentioning

confidence: 99%

Frequentist operating characteristics of Bayesian optimal designs via simulation

Zhang

Trippa

Parmigiani

2019

Statistics in Medicine

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Bayesian adaptive designs have become popular because of the possibility of increasing the number of patients treated with more beneficial treatments, while still providing sufficient evidence for treatment efficacy comparisons. It can be essential, for regulatory and other purposes, to conduct frequentist analyses both before and after a Bayesian adaptive trial, and these remain challenging. In this paper, we propose a general simulation-based approach to compare frequentist designs with Bayesian adaptive designs based on frequentist criteria such as power and to compute valid frequentist p-values. We illustrate our approach by comparing the power of an equal randomization (ER) design with that of an optimal Bayesian adaptive (OBA) design. The Bayesian design considered here is the dynamic programming solution of the optimization of a specific utility function defined by the number of successes in a patient horizon, including patients whose treatment will be affected by the trial's results after the end of the trial.While the power of an ER design depends on treatment efficacy and the sample size, the power of the OBA design also depends on the patient horizon size. Our results quantify the trade-off between power and the optimal assignment of patients to treatments within the trial. We show that, for large patient horizons, the two criteria are in agreement, while for small horizons, differences can be substantial. This has implications for precision medicine, where patient horizons are decreasing as a result of increasing stratification of patients into subpopulations defined by molecular markers. KEYWORDSBayesian adaptive designs, dynamic programming, frequentist analyses, optimal strategy Spiegelhalter et al 5 and Cellamare et al 6 have illustrated the use of Bayesian approaches in clinical trials and discussed their advantages. Ethical concerns 7 associated with adaptive designs are also relevant in Bayesian adaptive settings. On the other hand, frequentist criteria remain the mainstay in regulatory decision-making and in the medical literature. This applies to both trial design requirements, such as the power at a given type I error level, and reported 4026

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Unbiased estimation for response adaptive clinical trials

Cited by 46 publications

References 42 publications

Response‐adaptive designs for binary responses: How to offer patient benefit while being robust to time trends?

Response‐adaptive designs for binary responses: How to offer patient benefit while being robust to time trends?

Bayesian Response Adaptive Randomization in Clinical Trials With a Time-trend: Application to a Real Trial

Frequentist operating characteristics of Bayesian optimal designs via simulation

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