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Activity Number: 641
Type: Topic Contributed
Date/Time: Thursday, August 13, 2015 : 8:30 AM to 10:20 AM
Sponsor: ENAR
Abstract #315669
Title: Accounting for Complex Sampling in Survival Analyses of Screening Data
Author(s): Noorie Hyun* and Li Cheung and Qing Pan and Hormuzd Katki
Companies: National Cancer Institute and The George Washington University and The George Washington University and National Cancer Institute
Keywords: survey sampling ; survival analysis ; screening data ; Cox regression ; logistic regression ; mixture model
Abstract:

Screening programs have 2 key features: incident disease onset is interval-censored between visits, and disease diagnosed at follow-up visits could be incident disease or undiagnosed disease present at baseline. We have shown that standard Kaplan-Meier or Cox model analyses, which ignore those features, generally underestimate early risks and overestimate later risks. We proposed logistic-Weibull, and semiparametric logistic-Cox, mixture models to account for those features. Another feature of efficient research studies conducted in screening programs is that covariate ascertainment only occurs on a complex subsample of participants, such as nested case-control, case-cohort, or more complex designs. We extend the logistic-Weibull and logistic-Cox models to account for complex sampling. We discuss efficient sampling designs that oversample the most informative participants. We apply our model to data from the cervical cancer screening program at Kaiser Permanente Northern California, where HPV and Pap tests are available on everyone, but novel tests (HPV genotyping, p16 staining, HPV genome methylation and sequencing) are conducted on only on a complex subsample.


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

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