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Activity Number: 375
Type: Contributed
Date/Time: Tuesday, August 2, 2016 : 10:30 AM to 12:20 PM
Sponsor: Section on Statistics in Epidemiology
Abstract #320351
Title: Semiparametric Mixture Models for Left-Censored and Irregularly Interval-Censored Data: Application to a Cohort Assembled from Electronic Health Records
Author(s): Noorie Hyun* and Li Cheung and Qing Pan and Mark Schiffman and Hormuzd Katki
Companies: National Cancer Institute and The George Washington University and The George Washington University and National Cancer Institute and National Cancer Institute
Keywords: Mixture model ; Interval-censored data ; Sampling design ; Health record data

For cost-effectiveness and efficiency, large-scale epidemiologic cohorts are being assembled within large health-care providers who use electronic health records. For estimating absolute risk from such cohorts, there are challenges that incident disease is interval-censored between irregular visits and prevalent disease is left-censored. Furthermore, because prevalent disease is not always immediately diagnosed, disease diagnosed at future visits is a mixture of truly incidence disease and undiagnosed prevalent disease. The second challenge we address is to estimate absolute risk from phase 2 case-control studies nested within such cohorts. We propose inverse-probability weighted (IPW) prevalence-incidence models, which are mixture models for interval-censored incident disease and point-mass left-censored prevalent disease. We employ iterative algorithms to estimate parameters for semiparametric and weakly-parametric prevalence-incidence models. We apply the models to subcohort sample cervical cancer screening data at Kaiser Permanente Northern California and calculate absolute risks of cervical precancer/cancer for different human papillomavirus types.

Authors who are presenting talks have a * after their name.

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