Asymptotically Unbiased Estimation of Exposure Odds Ratios in Complete Records Logistic Regression.

ABSTRACT: Missing data are a commonly occurring threat to the validity and efficiency of epidemiologic studies. Perhaps the most common approach to handling missing data is to simply drop those records with 1 or more missing values, in so-called "complete records" or "complete case" analysis. In this paper, we bring together earlier-derived yet perhaps now somewhat neglected results which show that a logistic regression complete records analysis can provide asymptotically unbiased estimates of the association of an exposure of interest with an outcome, adjusted for a number of confounders, under a surprisingly wide range of missing-data assumptions. We give detailed guidance describing how the observed data can be used to judge the plausibility of these assumptions. The results mean that in large ep

SUBMITTER: Bartlett JW 

PROVIDER: S-EPMC4597800 | biostudies-literature | 2015 Oct

REPOSITORIES: biostudies-literature