Instability of inverse probability weighting methods and a remedy for non-ignorable missing data

TL;DR

This paper identifies the instability issues of inverse probability weighting methods in non-ignorable missing data analysis and proposes a semiparametric modeling approach with a maximum likelihood estimation to improve stability and accuracy.

Contribution

The paper introduces a novel semiparametric method that avoids the instability of IPW by modeling outcome distribution and missingness jointly with a maximum likelihood approach.

Findings

Proposed method outperforms existing methods in simulations

The approach is robust to high missingness probabilities

Real data examples demonstrate practical advantages

Abstract

Inverse probability weighting (IPW) methods are commonly used to analyze non-ignorable missing data under the assumption of a logistic model for the missingness probability. However, solving IPW equations numerically may involve non-convergence problems when the sample size is moderate and the missingness probability is high. Moreover, those equations often have multiple roots, and identifying the best root is challenging. Therefore, IPW methods may have low efficiency or even produce biased results. We identify the pitfall in these methods pathologically: they involve the estimation of a moment-generating function, and such functions are notoriously unstable in general. As a remedy, we model the outcome distribution given the covariates of the completely observed individuals semiparametrically. After forming an induced logistic regression model for the missingness status of the outcome and covariate, we develop a maximum conditional likelihood method to estimate the underlying parameters. The proposed method circumvents the estimation of a moment-generating function and hence overcomes the instability of IPW methods. Our theoretical and simulation results show that the proposed method outperforms existing competitors greatly. Two real data examples are analyzed to illustrate the advantages of our method. We conclude that if only a parametric logistic regression is assumed but the outcome regression model is left arbitrary, then one has to be cautious in using any of the existing statistical methods in problems involving non-ignorable missing data.