Much ado about nothing: A comparison of missing data methods and software to fit incomplete data regression models

Abstract

Missing data are a recurring problem that can cause bias or lead to inefficient analyses. Development of statistical methods to address missingness have been actively pursued in recent years, including imputation, likelihood and weighting approaches. Each approach is more complicated when there are many patterns of missing values, or when both categorical and continuous random variables are involved. Implementations of routines to incorporate observations with incomplete variables in regression models are now widely available. We review these routines in the context of a motivating example from a large health services research dataset. While there are still limitations to the current implementations, and additional efforts are required of the analyst, it is feasible to incorporate partially observed values, and these methods should be utilized in practice.

Figure 1

Monotone and non-monotone patterns of missingness (Obs=observed, M=missing)

Figure 2

Use of Likelihood based approach with EM algorithm to incorporate partially

Figure 3

Proposed guidelines for reporting missing covariate data (Burton and Altman 2004)

Figure 4

Description of missing data (using Stata misschk function)