The calculation of quality of care measures based on electronic medical records (EMRs) may be inaccurate because of incomplete capture of past services. We evaluate the influence of different statistical approaches for calculating the proportion of patients who are up-to-date for a preventive service, using the example of colorectal cancer (CRC) screening. We propose an extension of traditional mixture models to account for the uncertainty in compliance, which is further complicated by the choice of various screening modalities with different recommended screening intervals. We conducted simulation studies to compare various statistical approaches and demonstrated that the proposed method can alleviate bias when individuals with complete prior medical history information were not representative of the targeted population. The method is motivated by and applied to data from the National Cancer Institute-funded consortium Population-Based Research Optimizing Screening through Personalized Regiments (PROSPR). Findings from the application are important for the evaluation of appropriate use of preventive care and provide a novel tool for dealing with similar analytical challenges with EMR data in broad settings.
Keywords: Cancer Screening; EMR data; Event-time analysis; Mixture Model.