Futility analyses for the MCP-Mod methodology based on longitudinal models

TL;DR

This paper develops and evaluates futility analysis methods for MCP-Mod in longitudinal models, demonstrating improved decision-making with longitudinal data over complete-case analysis through simulations and real data application.

Contribution

It introduces formulas for predictive and conditional power in MCP-Mod with longitudinal models, enabling better interim decision-making using incomplete data.

Findings

Longitudinal analysis outperforms completer-only analysis, especially with higher recruitment speed and larger correlation.

Proposed methods perform adequately in simulation studies.

Application to real asthma data illustrates practical utility.

Abstract

This article discusses futility analyses for the MCP-Mod methodology. Formulas are derived for calculating predictive and conditional power for MCP-Mod, which also cover the case when longitudinal models are used allowing to utilize incomplete data from patients at interim. A simulation study is conducted to evaluate the repeated sampling properties of the proposed decision rules and to assess the benefit of using a longitudinal versus a completer only model for decision making at interim. The results suggest that the proposed methods perform adequately and a longitudinal analysis outperforms a completer only analysis, particularly when the recruitment speed is higher and the correlation over time is larger. The proposed methodology is illustrated using real data from a dose-finding study for severe uncontrolled asthma.