Title: Sensitivity analysis for missing not at random data in electronic health records-based research
Authors: Tanayott Thaweethai - Massachusetts General Hospital (United States) [presenting]
Sebastien Haneuse - Harvard TH Chan School of Public Health (United States)
Abstract: While electronic health records (EHR) present a rich and promising data source for conducting observational research, they are highly susceptible to missingness due to the complex process by which EHR are collected and generated. Even worse, data in EHR is frequently missing not at random (MNAR); e.g., whether a given laboratory test is ordered is often correlated with the expected value of the test. Building off a novel framework for handling missing data in EHR based on a modularization of the data provenance (i.e., the process by which data is observed in EHR), we present a method for localizing sensitivity to MNAR data to specific decisions or actions made by patients, their healthcare providers, or the larger healthcare system. We conclude with novel strategies for interpreting the results of multidimensional sensitivity analyses for MNAR data.