Adaptive Seamless Design for Establishing Pharmacokinetic and Efficacy Equivalence in Developing Biosimilars

Uozumi, Ryuji; Hamada, Chikuma

doi:10.1177/2168479017706526

Cited by 3 publications

(2 citation statements)

References 45 publications

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“…A simulation study investigating a seamless phase II/ III trial with promising zone design suggested some appealing advantages over classical designs including time, cost and sample size savings, which could speed up the development of biosimilars [17]. Additionally, it has been suggested that promising zone designs could be particularly useful in early-stage exploratory studies where very little is known about the treatment effect [18], and in clinical trials of rare diseases [19].…”

Section: Strengthsmentioning

confidence: 99%

A systematic review of the “promising zone” design

Edwards

Walters

Kunz

et al. 2020

Trials

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Introduction Sample size calculations require assumptions regarding treatment response and variability. Incorrect assumptions can result in under- or overpowered trials, posing ethical concerns. Sample size re-estimation (SSR) methods investigate the validity of these assumptions and increase the sample size if necessary. The “promising zone” (Mehta and Pocock, Stat Med 30:3267–3284, 2011) concept is appealing to researchers for its design simplicity. However, it is still relatively new in the application and has been a source of controversy. Objectives This research aims to synthesise current approaches and practical implementation of the promising zone design. Methods This systematic review comprehensively identifies the reporting of methodological research and of clinical trials using promising zone. Databases were searched according to a pre-specified search strategy, and pearl growing techniques implemented. Results The combined search methods resulted in 270 unique records identified; 171 were included in the review, of which 30 were trials. The median time to the interim analysis was 60% of the original target sample size (IQR 41–73%). Of the 15 completed trials, 7 increased their sample size. Only 21 studies reported the maximum sample size that would be considered, for which the median increase was 50% (IQR 35–100%). Conclusions Promising zone is being implemented in a range of trials worldwide, albeit in low numbers. Identifying trials using promising zone was difficult due to the lack of reporting of SSR methodology. Even when SSR methodology was reported, some had key interim analysis details missing, and only eight papers provided promising zone ranges.

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Section: Strengthsmentioning

confidence: 99%

A systematic review of the “promising zone” design

Edwards

Walters

Kunz

et al. 2020

Trials

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“…Pan et al 10 proposed a Bayesian group sequential design that integrates historical information through the calibrated power prior approach. Uozumi and Hamada 11 developed an adaptive seamless PK and efficacy design to combine the tests of PK parameters and efficacy. Weiss et al 12 proposed to incorporate prior information from the historical data of the reference product, and the results from in vitro and phase I PK/PD study of the biosimilar.…”

Section: Introductionmentioning

confidence: 99%

BOB: Bayesian optimal design for biosimilar trials with co‐primary endpoints

Chi

Lin

2022

Statistics in Medicine

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For regulatory approval of a biosimilar product, extensive evaluations should be performed by rigorous clinical trials to establish the similarity between the reference product and the proposed biosimilar in terms of both efficacy and safety.Existing designs for biosimilar trials often use a single primary efficacy endpoint in trial monitoring, and then separately evaluate the safety of the biosimilar product in a secondary analysis at the trial completion. However, ignoring the safety endpoint and the correlation between safety and efficacy in trial monitoring may lead to a high false positive rate, or it may delay the termination of the trial when dissimilarity in safety is early detected. We propose a Bayesian optimal design for biosimilar trials by incorporating both safety and efficacy endpoints in a unified framework. Based on a Bayesian joint safety and efficacy model, we sequentially use a so-called Bayesian biosimilar probability to make go/no-go decisions. We calibrate the Bayesian design to maximize the statistical power while maintaining the frequentist type I error rate at the nominal level.We carry out extensive simulation studies to show that the design has desirable performance in terms of the false positive rate and the average sample size. We also apply the proposed design to a biosimilar trial evaluating a ranibizumab product.

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