On the Nature of Informative Presence Bias in Analyses of Electronic Health Records.
Epidemiology
; 33(1): 105-113, 2022 01 01.
Article
in En
| MEDLINE
| ID: mdl-34711733
ABSTRACT
Electronic health records (EHRs) offer unprecedented opportunities to answer epidemiologic questions. However, unlike in ordinary cohort studies or randomized trials, EHR data are collected somewhat idiosyncratically. In particular, patients who have more contact with the medical system have more opportunities to receive diagnoses, which are then recorded in their EHRs. The goal of this article is to shed light on the nature and scope of this phenomenon, known as informative presence, which can bias estimates of associations. We show how this can be characterized as an instance of misclassification bias. As a consequence, we show that informative presence bias can occur in a broader range of settings than previously thought, and that simple adjustment for the number of visits as a confounder may not fully correct for bias. Additionally, where previous work has considered only underdiagnosis, investigators are often concerned about overdiagnosis; we show how this changes the settings in which bias manifests. We report on a comprehensive series of simulations to shed light on when to expect informative presence bias, how it can be mitigated in some cases, and cases in which new methods need to be developed.
Full text:
1
Database:
MEDLINE
Main subject:
Electronic Health Records
Type of study:
Clinical_trials
/
Etiology_studies
/
Incidence_studies
/
Observational_studies
/
Risk_factors_studies
Limits:
Humans
Language:
En
Journal:
Epidemiology
Journal subject:
EPIDEMIOLOGIA
Year:
2022
Type:
Article
Affiliation country:
Canada