Graph Based, Adaptive, Multi Arm, Multiple Endpoint, Two Stage Design

TL;DR

This paper extends graph-based multiple testing designs to two-stage adaptive procedures, enabling early treatment efficacy detection, sample size adjustments, and hypothesis modifications while controlling the family-wise error rate.

Contribution

It introduces two methods for maintaining error control in adaptive, multi-endpoint, two-stage graph-based testing strategies, with detailed methodology and comparative simulation results.

Findings

Both methods effectively control the family-wise error rate.

Simulation shows comparable operating characteristics for the two approaches.

The extended design allows flexible, efficient adaptive testing with multiple endpoints.

Abstract

The graph based approach to multiple testing is an intuitive method that enables a study team to represent clearly, through a directed graph, its priorities for hierarchical testing of multiple hypotheses, and for propagating the available type-1 error from rejected or dropped hypotheses to hypotheses yet to be tested. Although originally developed for single stage non-adaptive designs, we show how it may be extended to two-stage designs that permit early identification of efficacious treatments, adaptive sample size re-estimation, dropping of hypotheses, and changes in the hierarchical testing strategy at the end of stage one. Two approaches are available for preserving the family wise error rate in the presence of these adaptive changes; the p-value combination method, and the conditional error rate method. In this investigation we will present the statistical methodology underlying each approach and will compare the operating characteristics of the two methods in a large simulation experiment.