{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"submitter":["Bartlett JW"],"funding":["Economic and Social Research Council","US National Institutes of Health","United Kingdom Medical Research Council","US National Institute of Mental Health","NIMH NIH HHS","Medical Research Council","National Institute of Mental Health"],"pagination":["730-6"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC4597800"],"repository":["biostudies-literature"],"omics_type":["Unknown"],"volume":["182(8)"],"pubmed_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"],"journal":["American journal of epidemiology"],"pubmed_title":["Asymptotically Unbiased Estimation of Exposure Odds Ratios in Complete Records Logistic Regression."],"pmcid":["PMC4597800"],"funding_grant_id":["MC_UU_12023/21","K01MH087219","G0900724","MR/K02180X/1","K01 MH087219","ES/G026300/1"],"pubmed_authors":["Harel O","Carpenter JR","Bartlett JW"],"additional_accession":[]},"is_claimable":false,"name":"Asymptotically Unbiased Estimation of Exposure Odds Ratios in Complete Records Logistic Regression.","description":"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","dates":{"release":"2015-01-01T00:00:00Z","publication":"2015 Oct","modification":"2025-04-04T10:50:21.175Z","creation":"2019-03-27T01:59:43Z"},"accession":"S-EPMC4597800","cross_references":{"pubmed":["26429998"],"doi":["10.1093/aje/kwv114"]}}