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Activity Number:
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344
- Semiparametric Modeling
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Type:
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Contributed
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Date/Time:
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Tuesday, July 31, 2018 : 10:30 AM to 12:20 PM
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Sponsor:
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Biometrics Section
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Abstract #330373
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Presentation
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Title:
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Sample-Weighted Semiparametric Estimates of Cause-Specific Cumulative Incidence Using Left-/Interval Censored Data from Electronic Health Records
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Author(s):
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Noorie Hyun* and Hormuzd A. Katki and Barry Ira Graubard
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Companies:
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Medical College of Wisconsin and Biostatistics Branch, Division of Cancer Epidemiology & Genetics, National Cancer Institute and National Cancer Institute
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Keywords:
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Competing risks;
Interval censoring;
Stratified random sample;
Semiparametric models;
Bootstrap;
Electronic health records
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Abstract:
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Electronic health records (EHRs) are cost-effective data source for developing disease-specific risk models while accounting for competing outcomes. However, there are challenges in applying existing risk models to EHRs: left-censoring of prevalent disease, interval-censoring of incident disease and uncertainty of disease prevalence when definitive disease ascertainment is not conducted at baseline. Furthermore, expensive or novel bio-assay tests cannot be conducted on everyone but only on stratified random subsamples from EHRs with varying sampling fraction. We propose a family of semiparametric mixture models for estimating cause-specific cumulative risk/incidence conditional on covariates. We adopt the iterative convex minorant algorithms to nonparametrically estimate cumulative incidence functions of competing outcomes and employ a smoothed bootstrap sample-weighted procedure for consistent confidence interval estimation of a cumulative risk/incidence, of which the asymptotic distribution is not normal. We applied the proposed method to stratified subsamples from EHRs at Kaiser Permanente Northern California to calculate cumulative incidences for competing outcomes.
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Authors who are presenting talks have a * after their name.
