A proposed statistical framework for the management of subgroup analyses for large clinical trials

Abstract

Planned and unplanned subgroup analyses of large clinical trials are frequently performed and the results are sometimes difficult to interpret. The source of a nominal significant finding may come from a true signal, variation of the clinical trial outcome or the observed data structure. Quantitative assessment is critical to the interpretation of the totality of the clinical data. In this article we provide a general framework to manage subgroup analyses and to interpret the findings through a set of supplement analyses to planned main (primary and secondary) analyses, as an alternative to the commonly used multiple comparison framework. The proposed approach collectively and coherently utilizes several quantitative methods and enhances the credibility and interpretability of subgroup analyses. A case study is used to illustrate the application of the proposed method.