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

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.