Data from Electronic Health Records (EHR) are a valuable research tool, providing information on outcomes
and exposures that would be costly and difficult to obtain through primary data collection. However, EHR data
capture is driven by clinical and administrative rather than research needs, necessitating substantial
methodological innovation to obtain valid results. While a number of prior methodological studies have focused
on reducing confounding in observational studies conducted using EHR data, they have not considered the risk
of residual confounding that results when confounder variables are measured with error. The proposed study
will develop novel statistical tools tailored to the EHR context to address measurement error and missing data
in confounders. Under Aim 1 we will use a recently developed statistical approach, integrated likelihood, to
develop a method for confounder control using imperfect confounders that does not require validation data.
Under Aim 2, we will develop an index of sensitivity of study results to the assumption of “informative
presence,” i.e. that absence of information on a confounder is indicative of absence of the confounder. Novel
methods will be evaluated and compared to standard approaches using simulated data and applied to existing
data from a study of colon cancer recurrence. Statistical software code for these methods will be developed in
the R programming language and disseminated via our project website and Github. This research will provide
methodological tools to improve the validity of results obtained through secondary analysis of EHR-derived
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