Robust Emax Model Fitting: Addressing Nonignorable Missing Binary Outcome in Dose-Response Analysis

TL;DR

This paper introduces a penalized likelihood method with a modified EM algorithm to accurately fit the Binary Emax dose-response model in the presence of nonignorable missing data and separation issues, improving bias reduction.

Contribution

It proposes a novel penalized likelihood approach with a Jeffreys prior and a modified EM algorithm to handle nonignorable missing data and separation in dose-response analysis.

Findings

Outperforms existing methods like NRI in simulations

Reduces bias in parameter estimation

Effective in real Phase II clinical trial data

Abstract

The Binary Emax model is widely employed in dose-response analysis during drug development, where missing data often pose significant challenges. Addressing nonignorable missing binary responses, where the likelihood of missing data is related to unobserved outcomes, is particularly important, yet existing methods often lead to biased estimates. This issue is compounded when using the regulatory-recommended imputing as treatment failure approach, known as non-responder imputation. Moreover, the problem of separation, where a predictor perfectly distinguishes between outcome classes, can further complicate likelihood maximization. In this paper, we introduce a penalized likelihood-based method that integrates a modified Expectation-Maximization algorithm in the spirit of Ibrahim and Lipsitz to effectively manage both nonignorable missing data and separation issues. Our approach applies a noninformative Jeffreys prior to the likelihood, reducing bias in parameter estimation. Simulation studies demonstrate that our method outperforms existing methods, such as NRI, and the superiority is further supported by its application to data from a Phase II clinical trial. Additionally, we have developed an R package, ememax, to facilitate the implementation of the proposed method.