title: Using Rich Data on Comorbidities in Case-Control Study Design with Electronic Health Record Data Improves Control of Confounding in the Detection of Adverse Drug Reactions.
abstract: Recent research has suggested that the case-control study design, unlike the self-controlled study design, performs poorly in controlling confounding in the detection of adverse drug reactions (ADRs) from administrative claims and electronic health record (EHR) data, resulting in biased estimates of the causal effects of drugs on health outcomes of interest (HOI) and inaccurate confidence intervals. Here we show that using rich data on comorbidities and automatic variable selection strategies for selecting confounders can better control confounding within a case-control study design and provide a more solid basis for inference regarding the causal effects of drugs on HOIs. Four HOIs are examined: acute kidney injury, acute liver injury, acute myocardial infarction and gastrointestinal ulcer hospitalization. For each of these HOIs we use a previously published reference set of positive and negative control drugs to evaluate the performance of our methods. Our methods have AUCs that are often substantially higher than the AUCs of a baseline method that only uses demographic characteristics for confounding control. Our methods also give confidence intervals for causal effect parameters that cover the expected no effect value substantially more often than this baseline method. The case-control study design, unlike the self-controlled study design, can be used in the fairly typical setting of EHR databases without longitudinal information on patients. With our variable selection method, these databases can be more effectively used for the detection of ADRs.
authors.affiliation: Department of Biostatistics, Columbia University, New York, New York, United States of America.
authors.forename: Daniel
authors.identifier: http://orcid.org/0000-0002-9581-8870
authors.initials: D
authors.lastname: Backenroth
authors.affiliation: Department of Biomedical Informatics, Columbia University, New York, New York, United States of America.
authors.forename: Herbert
authors.initials: H
authors.lastname: Chase
authors.affiliation: Department of Biomedical Informatics, Columbia University, New York, New York, United States of America.
authors.forename: Carol
authors.initials: C
authors.lastname: Friedman
authors.affiliation: Department of Biostatistics, Columbia University, New York, New York, United States of America.
authors.forename: Ying
authors.initials: Y
authors.lastname: Wei
mesh_terms: D058186:Acute Kidney Injury / Q000188:drug therapy; D000328:Adult; D016907:Adverse Drug Reaction Reporting Systems; D000368:Aged; D019540:Area Under Curve; D016022:Case-Control Studies; D015897:Comorbidity; D016208:Databases, Factual; D064420:Drug-Related Side Effects and Adverse Reactions / Q000175:diagnosis*; D057286:Electronic Health Records; D005260:Female; D006801:Humans; D008297:Male; D008875:Middle Aged; D009203:Myocardial Infarction / Q000188:drug therapy; D012107:Research Design; D018570:Risk Assessment / Q000379:methods*