Informative presence bias in analyses of electronic health records-derived data: a cautionary note. (19th April 2022)

• Record Type: Journal Article
• Authors: Harton, Joanna; Mitra, Nandita; Hubbard, Rebecca A
• Is Part Of: Journal of the American Medical Informatics Association. Volume 29:Number 7(2022)
• Page Start: 1191
• Page End: 1199
• Publication Date: 2022-04-19
• Subjects: longitudinal data -- survival analysis -- electronic health records -- misclassification -- bias; 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/ocac050
• Languages: English
• ISSNs: 1067-5027

Abstract

Abstract: Objective: Electronic health record (EHR)-derived data are extensively used in health research. However, the pattern of patient interaction with the healthcare system can result in informative presence bias if those who have poorer health have more data recorded than healthier patients. We aimed to determine how informative presence affects bias across multiple scenarios informed by real-world healthcare utilization patterns. Materials and methods: We conducted an analysis of EHR data from a pediatric healthcare system as well as simulation studies to characterize conditions under which informative presence bias is likely to occur. This analysis extends prior work by examining a variety of scenarios for the relationship between a biomarker and a health event of interest and the healthcare visit process. Results: Using biomarker values gathered at both informative and noninformative visits when estimating the effect of the biomarker on the event of interest resulted in minimal bias when the biomarker was relatively stable over time but produced substantial bias when the biomarker was more volatile. Adjusting analyses for the number of prior visits within a fixed look-back window was able to reduce but not eliminate this bias. Discussion: These results suggest that bias may arise frequently in commonly encountered scenarios and may not be eliminated by adjusting for prior visit intensity. Conclusion: Depending on the context, the estimated effect from analyses using data from all visits available may diverge from the true effect. Sensitivity analyses using only visits likely to be informative or noninformative based on visit type may aid in the assessment of the magnitude of potential bias.