Empirical Evaluations of Clinical Trial Designs

Trippa, Lorenzo; Bacallado, Sergio

doi:10.1001/jamanetworkopen.2022.11620

JAMA Netw Open

2022

DOI: 10.1001/jamanetworkopen.2022.11620

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Empirical Evaluations of Clinical Trial Designs

Lorenzo Trippa

Sergio Bacallado

Abstract: Broglio et al 1 present an insightful comparison of 2 clinical trial designs: (1) the actual study design that was implemented in the Stroke Hyperglycemia Insulin Network Effort (SHINE) trial 2 and (2) an alternative nonimplemented design. This comparison was planned before the SHINE study started + Related articleAuthor affiliations and article information are listed at the end of this article.

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2024

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Uncertainty directed factorial clinical trials

Kotecha,

Ventz,

Fortini

et al. 2024

Biostatistics

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Summary The development and evaluation of novel treatment combinations is a key component of modern clinical research. The primary goals of factorial clinical trials of treatment combinations range from the estimation of intervention-specific effects, or the discovery of potential synergies, to the identification of combinations with the highest response probabilities. Most factorial studies use balanced or block randomization, with an equal number of patients assigned to each treatment combination, irrespective of the specific goals of the trial. Here, we introduce a class of Bayesian response-adaptive designs for factorial clinical trials with binary outcomes. The study design was developed using Bayesian decision-theoretic arguments and adapts the randomization probabilities to treatment combinations during the enrollment period based on the available data. Our approach enables the investigator to specify a utility function representative of the aims of the trial, and the Bayesian response-adaptive randomization algorithm aims to maximize this utility function. We considered several utility functions and factorial designs tailored to them. Then, we conducted a comparative simulation study to illustrate relevant differences of key operating characteristics across the resulting designs. We also investigated the asymptotic behavior of the proposed adaptive designs. We also used data summaries from three recent factorial trials in perioperative care, smoking cessation, and infectious disease prevention to define realistic simulation scenarios and illustrate advantages of the introduced trial designs compared to other study designs.

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Uncertainty directed factorial clinical trials

Kotecha,

Ventz,

Fortini

et al. 2024

Biostatistics

View full text Add to dashboard Cite

show abstract

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Empirical Evaluations of Clinical Trial Designs

Cited by 1 publication

References 7 publications

Uncertainty directed factorial clinical trials

Uncertainty directed factorial clinical trials

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