Bias Reduction in Logistic Regression with Missing Responses When the Missing Data Mechanism is Nonignorable

ABSTRACT

In logistic regression with nonignorable missing responses, Ibrahim and Lipsitz proposed a method for estimating regression parameters. It is known that the regression estimates obtained by using this method are biased when the sample size is small. Also, another complexity arises when the iterative estimation process encounters separation in estimating regression coefficients. In this article, we propose a method to improve the estimation of regression coefficients. In our likelihood-based method, we penalize the likelihood by multiplying it by a noninformative Jeffreys prior as a penalty term. The proposed method reduces bias and is able to handle the issue of separation. Simulation results show substantial bias reduction for the proposed method as compared to the existing method. Analyses using real world data also support the simulation findings. An R package called brlrmr is developed implementing the proposed method and the Ibrahim and Lipsitz method.

KEYWORDS:

• EM algorithm
• Firth correction
• Missing binary response
• Nonignorable missing
• Separation

Acknowledgments

We are grateful to Dr. Brenton Kenkel for sharing the dataset, and we thank Dr. Kosalaram Goteti and Lakshmi Rangachari for their help. We acknowledge the valuable comments from the referees and the insightful suggestions from the Editor Dr. Nicole Lazar, which considerably improved this article.