Early termination in single-parameter model phase II clinical trial designs using decreasingly informative priors

Chen Wang, Roy T. Sabo, Nitai D. Mukhopadhyay, Robert A. Perera

Abstract

Background: To exchange the type of subjective Bayesian prior selection for assumptions more directly related to statistical decision making in clinician studies and trials, the decreasingly informative prior (DIP) is considered. We expand standard Bayesian early termination methods in one-parameter statistical models for Phase II clinical trials to include decreasingly informative priors (DIP). These priors are designed to reduce the chance of erroneously adapting trials too early by parameterize skepticism in an amount always equal to the unobserved sample size.

Method: We show how to parameterize these priors based on effective prior sample size and provide examples for common single-parameter models, include Bernoulli, Poisson, and Gaussian distributions. We use a simulation study to search through possible values of total sample sizes and termination thresholds to find the smallest total sample size (N) under admissible designs, which we define as having at least 80% power and no greater than 5% type I error rate.

Results: For Bernoulli, Poisson, and Gaussian distributions, the DIP approach requires fewer patients when admissible designs are achieved. In situations where type I error or power are not admissible, the DIP approach yields similar power and better-controlled type I error with comparable or fewer patients than other Bayesian priors by Thall and Simon.

Conclusions: The DIP helps control type I error rates with comparable or fewer patients, especially for those instances when increased type I error rates arise from erroneous termination early in a trial.

Keywords

Bayesian methods, Early termination, Hypothesis testing, Phase II clinical trial

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References

Peter F. Thall and Richard Simon. Practical Bayesian guidelines for phase iib clinical trials. Biometrics. 1994;50(2):337-49.

Peter F. Thall and Richard Simon. A Bayesian approach to establishing sample size and monitoring criteria for phase ii clinical trials. Controlled Clin Trials. 1994;15(6):463-81.

U. S. Department of Health, Human Services Food, and Drug Administration. Guidance for the use of Bayesian statistics in medical device clinical trials. 2010.

Sabo RT. Adaptive allocation for binary outcomes using decreasingly informative priors. J Biopharmaceutical Statistics. 2014;24(3):569-78.

Donahue E, Sabo RT. A natural lead-in approach to response-adaptive allocation for continuous outcomes. Pharmaceutical Statistics. 2021;20(3):563-72.

Sabo RT, Bello G. Optimal and lead-in adaptive allocation for binary outcomes: a comparison of Bayesian methodologies. Communications in Stat Theory Methods. 2017;46(6):2823-36.

Neuenschwander B, Weber S, Schmidli H, Hagan AO. Predictively consistent prior effective sample sizes. Biometrics. 2020;76:578-87.

Morita S, Thall PF, Muller P. Determining the effective sample size of a parametric prior. Biometrics. 2008;64:595-602.

Plummer M, Stukalov A, Denwood M. rjags: Bayesian Graphical Models using MCMC, 2021. Available at: https://mcmc-jags.sourceforge.io. Accessed on 20 February 2022.