The Missing Covariate Indicator Method is Nearly Valid Almost Always

TL;DR

The paper demonstrates that the missing covariate indicator method (MCIM) is nearly unbiased in most epidemiological settings, especially when missingness is independent of the outcome, supporting its continued use.

Contribution

It provides a formal assessment of bias in MCIM and identifies conditions under which it remains nearly valid, offering guidance for epidemiological analyses.

Findings

MCIM bias is independent of disease rate and exposure-outcome association.

Bias depends on covariate prevalence, missingness, and associations.

MCIM is unbiased when the covariate is a risk factor but not a confounder.

Abstract

Background: Although the missing covariate indicator method (MCIM) has been shown to be biased under extreme conditions, the degree and determinants of bias have not been formally assessed. We derived the formula for the relative bias in the MCIM and systematically investigated conditions under which bias arises. We found that the extent of bias is independent of both the disease rate and the exposure-outcome association, but it is a function of 5 parameters: exposure and covariate prevalences, covariate missingness proportion, and associations of covariate with exposure and outcome. The MCIM was unbiased when the missing covariate is a risk factor for the outcome but not a confounder. The average median relative bias was zero across each of the parameters over a wide range of values considered. Our simulation study demonstrated that the mean and median of relative bias of MCIM was comparable to that of the no missingness method, which used the full sample with complete information for all variables, as long as the missingness of covariate is independent of the outcome. When missingness was no greater than 50%, less than 5% of the scenarios considered had relative bias greater than 10%. In several analyses of the Harvard cohort studies, the MCIM produced materially the same results as the multiple imputation method. In conclusion, the MCIM is nearly valid almost always in settings typically encountered in epidemiology and its continued use is recommended, unless the covariate is missing in an extreme proportion or acts as a strong confounder.