Improved inference for MCP-Mod approach using time-to-event endpoints with small sample sizes.

ABSTRACT: The Multiple Comparison Procedures with Modeling Techniques (MCP-Mod) framework has been recently approved by the U.S. Food, Administration, and European Medicines Agency as fit-for-purpose for phase II studies. Nonetheless, this approach relies on the asymptotic properties of Maximum Likelihood (ML) estimators, which might not be reasonable for small sample sizes. In this paper, we derived improved ML estimators and correction for their covariance matrices in the censored Weibull regression model based on the corrective and preventive approaches. We performed two simulation studies to evaluate ML and improved ML estimators with their covariance matrices in (i) a regression framework (ii) the Multiple Comparison Procedures with Modeling Techniques framework. We have shown that improved ML

SUBMITTER: Diniz MA 

PROVIDER: S-EPMC11457869 | biostudies-literature | 2023 Sep-Oct

REPOSITORIES: biostudies-literature