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Informed presence in electronic health record data: illustrating bias and bias reduction approaches in longitudinal analyses
Journal article   Peer reviewed

Informed presence in electronic health record data: illustrating bias and bias reduction approaches in longitudinal analyses

Daniel T Vader, Di Shu, Rebecca A Hubbard, Craig L K Boge, Anna Sharova, Kevin Downes and Yun Li
Epidemiology (Cambridge, Mass.)
18 Mar 2026
DOI: https://doi.org/10.1097/EDE.0000000000001974
PMID: 41849238

Abstract

informed presence bias bias reduction longitudinal study inverse intensity weighting electronic health record
Electronic health record (EHR) systems capture patient information inconsistently, with patients generally contributing more data when they are sick than healthy. This creates "informed presence," systematic differences between captured and non-captured data, potentially biasing association estimates. There is growing interest in methods that account for informed presence, but practical approaches for conceptualizing, identifying, and addressing this bias in applied EHR research have received limited attention. Focusing on longitudinal settings, we present a conceptual framework for informed presence bias, which arises when data capture depends on exposure and outcome and thus the visit process acts as a collider. We then illustrate methods that aim to reduce bias by reweighting or resampling observed data to approximate conditional independence between the visit process and the outcome. We illustrate these methods using longitudinal EHR data from pediatric solid organ transplant recipients (N=271) to examine the association between steroids and cytomegalovirus viremia, where the frequency of cytomegalovirus testing varies across patients and over time. Incidence rate ratios decreased from 1.83 (95% CI 1.02, 3.28) in a naïve analysis to 1.37 (0.73, 2.57) when accounting for informed presence using inverse intensity weighting. Incidence rate ratio estimates from bootstrapped inverse intensity weighting were 1.37 (0.71, 2.27) and 1.40 (0.73, 2.68) from multiple outputation. These results show the anticipated attenuation of effect estimates after accounting for informed presence bias. When analyzing irregularly measured EHR data, we recommend (1) identifying the expected observation process using conceptual diagrams, (2) assessing dependence in the observation process, and (3) accounting for outcome dependence in statistical analysis.

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