Mixture Models for Left- and Interval-Censored Data: Applications to a Cancer Screening Cohort Assembled from Electronic Health Records

• Activity Number: 412 - Applications and Methods for Risk Estimation
• Type: Contributed
• Date/Time: Tuesday, August 1, 2017 : 2:00 PM to 3:50 PM
• Sponsor: Section on Risk Analysis
• Author(s): Li Cheung* and Qing Pan and Noorie Hyun and Mark Schiffman and Barbara Fetterman and Philip Castle and Thomas Lorey and Hormuzd Katki
• Companies: National Cancer Institute and George Washington University and National Cancer Institute and National Cancer Institute and Kaiser Permanente Northern California and Albert Einstein School of Medicine and Kaiser Permanente Northern California and National Cancer Institute
• Keywords: cervical cancer ; cumulative risk estimation ; human papillomavirus ; Kaplan-Meier methods ; prevalent disease

Abstract

For cost-effectiveness and efficiency, many large-scale cohort studies are being assembled within health-care providers who use electronic health records. Two key features of such data are that incident disease is interval-censored between irregular visits and pre-existing (prevalent) disease is left-censored. Because prevalent disease is not always immediately diagnosed, some disease diagnosed at later visits are actually undiagnosed prevalent disease. We consider prevalent disease as a point mass at time zero for clinical applications where there is no interest in time of prevalent disease onset. We demonstrate that the naive Kaplan-Meier estimator underestimates risks at early time points and overestimates later risks. We propose a general family of mixture models that we call prevalence-incidence models. Parameters for parametric prevalence-incidence models are estimated by EM algorithm. Non-parametric methods are proposed to calculate cumulative risks for cases without covariates. We compare naive Kaplan-Meier, parametric, and non-parametric estimates of cumulative risk in the cervical cancer screening program at Kaiser Permanente Northern California.