Direct Estimation for Commonly Used Pattern-Mixture Models in Clinical Trials

TL;DR

This paper introduces an efficient direct likelihood estimation framework for common pattern-mixture models in clinical trials, addressing variance estimation challenges and demonstrating consistency through simulations and real data application.

Contribution

It proposes a novel analytical framework for direct likelihood estimation in pattern-mixture models, improving variance estimation and robustness over existing methods.

Findings

Proposed methods yield consistent estimators in simulations.

Framework applicable to various pattern-mixture models.

Demonstrated utility with clinical trial data.

Abstract

Pattern-mixture models have received increasing attention as they are commonly used to assess treatment effects in primary or sensitivity analyses for clinical trials with nonignorable missing data. Pattern-mixture models have traditionally been implemented using multiple imputation, where the variance estimation may be a challenge because the Rubin's approach of combining between- and within-imputation variance may not provide consistent variance estimation while bootstrap methods may be time-consuming. Direct likelihood-based approaches have been proposed in the literature and implemented for some pattern-mixture models, but the assumptions are sometimes restrictive, and the theoretical framework is fragile. In this article, we propose an analytical framework for an efficient direct likelihood estimation method for commonly used pattern-mixture models corresponding to return-to-baseline, jump-to-reference, placebo washout, and retrieved dropout imputations. A parsimonious tipping point analysis is also discussed and implemented. Results from simulation studies demonstrate that the proposed methods provide consistent estimators. We further illustrate the utility of the proposed methods using data from a clinical trial evaluating a treatment for type 2 diabetes.