Multi-treatment optimal response-adaptive designs for phase III clinical trials

Biswas, Atanu; Mandal, Saumen; Bhattacharya, Rahul

doi:10.1016/j.jkss.2010.04.004

Cited by 15 publications

(6 citation statements)

References 28 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…For instance, in trials with grave outcomes, there is a strong ethical requirement to minimize the total number of treatment failures in the trial while maintaining sufficient power of the test. A constrained optimization approach for deriving optimal allocations that provide trade-off between statistical (power) and ethical goals for a multi-arm binary outcome trial was originally proposed by Tymofyeyev et al [115], and subsequently extended by several authors in various contexts [9,15,54,111,131]. Baldi Antognini and Giovagnoli [8] also described in chapter 5 of their monograph a compound optimality approach that provides a trade-off among selected objectives by optimizing a weighted combination of the chosen criteria.…”

Section: Multi-arm Trialsmentioning

confidence: 99%

On Optimal Designs for Clinical Trials: An Updated Review

Sverdlov¹,

Ryeznik

Wong

2019

J Stat Theory Pract

View full text Add to dashboard Cite

Optimization of clinical trial designs can help investigators achieve higher quality results for the given resource constraints. The present paper gives an overview of optimal designs for various important problems that arise in different stages of clinical drug development, including phase I dose-toxicity studies; phase I/II studies that consider early efficacy and toxicity outcomes simultaneously; phase II dose-response studies driven by multiple comparisons (MCP), modeling techniques (Mod), or their combination (MCP-Mod); phase III randomized controlled multiarm multi-objective clinical trials to test difference among several treatment groups; and population pharmacokinetics-pharmacodynamics experiments. We find that modern literature is very rich with optimal design methodologies that can be utilized by clinical researchers to improve efficiency of drug development.

show abstract

Section: Multi-arm Trialsmentioning

confidence: 99%

On Optimal Designs for Clinical Trials: An Updated Review

Sverdlov¹,

Ryeznik

Wong

2019

J Stat Theory Pract

View full text Add to dashboard Cite

show abstract

“…al (2001); Hu and Rosenberger (2003); Bandyopadhyay and Bhattacharya (2006); ; Tymofyeyev et. al (2007); Biswas et. al (2010); Zhu and Hu (2010); Chambaz and van der Laan (2011)).…”

Section: Introductionmentioning

confidence: 98%

Are adaptive allocation designs beneficial for improving power in binary response trials?

Azriel,

Mandel,

Rinott

2011

Preprint

View full text Add to dashboard Cite

We consider the classical problem of selecting the best of two treatments in clinical trials with binary response. The target is to find the design that maximizes the power of the relevant test. Many papers use a normal approximation to the power function and claim that Neyman allocation that assigns subjects to treatment groups according to the ratio of the responses' standard deviations, should be used. As the standard deviations are unknown, an adaptive design is often recommended. The asymptotic justification of this approach is arguable, since it uses the normal approximation in tails where the error in the approximation is larger than the estimated quantity. We consider two different approaches for optimality of designs that are related to Pitman and Bahadur definitions of relative efficiency of tests. We prove that the optimal allocation according to the Pitman criterion is the balanced allocation and that the optimal allocation according to the Bahadur approach depends on the unknown parameters.Exact calculations reveal that the optimal allocation according to Bahadur is often close to the balanced design, and the powers of both are comparable to the Neyman allocation for small sample sizes and are generally better for large experiments. Our findings have important implications to the design of experiments, as the balanced design is proved to be optimal or * We thank Amir Dembo for helpful comments and in particular for deriving the exact expression for ν * in (4).1 close to optimal and the need for the complications involved in following an adaptive design for the purpose of increasing the power of tests is therefore questionable.

show abstract

“…[59] explored the derivation of optimal allocation proportion. Other discussions of the advantages of RAR can be found in [2,8,21,26,27,30,41]. Clinical trials using RAR designs include [1,42,53].…”

Section: Introductionmentioning

confidence: 99%

Seamless Clinical Trials with Doubly Adaptive Biased Coin Designs

Zhu¹,

Yu²,

Lai³

et al. 2023

The New England Journal of Statistics in Data Science

View full text Add to dashboard Cite

In addition to scientific questions, clinical trialists often explore or require other design features, such as increasing the power while controlling the type I error rate, minimizing unnecessary exposure to inferior treatments, and comparing multiple treatments in one clinical trial. We propose implementing adaptive seamless design (ASD) with response adaptive randomization (RAR) to satisfy various clinical trials’ design objectives. However, the combination of ASD and RAR poses a challenge in controlling the type I error rate. In this paper, we investigated how to utilize the advantages of the two adaptive methods and control the type I error rate. We offered the theoretical foundation for this procedure. Numerical studies demonstrated that our methods could achieve efficient and ethical objectives while controlling the type I error rate.

show abstract

Multi-treatment optimal response-adaptive designs for phase III clinical trials

Cited by 15 publications

References 28 publications

On Optimal Designs for Clinical Trials: An Updated Review

On Optimal Designs for Clinical Trials: An Updated Review

Are adaptive allocation designs beneficial for improving power in binary response trials?

Seamless Clinical Trials with Doubly Adaptive Biased Coin Designs

Contact Info

Product

Resources

About