biostudies-literatureBartlett JWEconomic and Social Research CouncilUS National Institutes of HealthUnited Kingdom Medical Research CouncilUS National Institute of Mental HealthNIMH NIH HHSMedical Research CouncilNational Institute of Mental Health730-6https://www.ebi.ac.uk/biostudies/studies/S-EPMC4597800biostudies-literatureUnknown182(8)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 epAmerican journal of epidemiologyAsymptotically Unbiased Estimation of Exposure Odds Ratios in Complete Records Logistic Regression.PMC4597800MC_UU_12023/21K01MH087219G0900724MR/K02180X/1K01 MH087219ES/G026300/1Harel OCarpenter JRBartlett JWfalseAsymptotically Unbiased Estimation of Exposure Odds Ratios in Complete Records Logistic Regression.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 ep2015-01-01T00:00:00Z2015 Oct2025-04-04T10:50:21.175Z2019-03-27T01:59:43ZS-EPMC45978002642999810.1093/aje/kwv114