A primer on quantitative bias analysis with positive predictive values in research using electronic health data. (31st July 2019)

• Record Type: Journal Article
• Authors: Newcomer, Sophia R; Xu, Stan; Kulldorff, Martin; Daley, Matthew F; Fireman, Bruce; Glanz, Jason M
• Is Part Of: Journal of the American Medical Informatics Association. Volume 26:Number 12(2019)
• Page Start: 1664
• Page End: 1674
• Publication Date: 2019-07-31
• Subjects: electronic health records -- outcome assessment -- bias -- medical informatics; Medical informatics -- Periodicals; Information Services -- Periodicals; Medical Informatics -- Periodicals; Médecine -- Informatique -- Périodiques; Informatica; Geneeskunde; Informatique médicale; Computer network resources; Electronic journals; 610.285
• DOI: 10.1093/jamia/ocz094
• Languages: English
• ISSNs: 1067-5027

Abstract

Abstract: Objective: In health informatics, there have been concerns with reuse of electronic health data for research, including potential bias from incorrect or incomplete outcome ascertainment. In this tutorial, we provide a concise review of predictive value–based quantitative bias analysis (QBA), which comprises epidemiologic methods that use estimates of data quality accuracy to quantify the bias caused by outcome misclassification. Target Audience: Health informaticians and investigators reusing large, electronic health data sources for research. Scope: When electronic health data are reused for research, validation of outcome case definitions is recommended, and positive predictive values (PPVs) are the most commonly reported measure. Typically, case definitions with high PPVs are considered to be appropriate for use in research. However, in some studies, even small amounts of misclassification can cause bias. In this tutorial, we introduce methods for quantifying this bias that use predictive values as inputs. Using epidemiologic principles and examples, we first describe how multiple factors influence misclassification bias, including outcome misclassification levels, outcome prevalence, and whether outcome misclassification levels are the same or different by exposure. We then review 2 predictive value–based QBA methods and why outcome PPVs should be stratified by exposure for bias assessment. Using simulations, we apply and evaluate the methods in hypothetical electronic health record–based immunization schedule safety studies. By providing an overview of predictive value–based QBA, we hope to bridge the disciplines of health informatics and epidemiology to inform how the impact of data quality issues can be quantified in research using electronic health data sources.