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 simulati

SUBMITTER: Wang C 

PROVIDER: S-EPMC9957559 | biostudies-literature | 2022 Apr-Jun

REPOSITORIES: biostudies-literature