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Activity Number: 555 - Using Surveys to Improve the Representativeness of Nonprobability Samples in Epidemiologic Studies
Type: Invited
Date/Time: Wednesday, August 1, 2018 : 2:00 PM to 3:50 PM
Sponsor: Section on Statistics in Epidemiology
Abstract #326733
Title: Population-Based Disease Risk Prediction Modeling Using National Survey, Clinical, and Registry Data: Application to Risk Prediction for Oropharyngeal Cancer in the US Population
Author(s): Barry Ira Graubard* and Anil Chaturvedi and Joseph Tota and Hormuzd A. Katki
Companies: National Cancer Institute and National Cancer Institute and National Cancer Institute and Biostatistics Branch, Division of Cancer Epidemiology & Genetics, National Cancer Institute
Keywords: absolute risk models; NHANES; cross-validation; propensity weighting

Population-based case-control studies (PBCCS) are important for obtaining nearly unbiased estimates of association of exposures and rare diseases for targeted populations. This paper discusses how to combine a clinically-based case series, a national survey as source of controls and a national-based disease surveillance system to calibrate the case series to form a national PBCCS that is used to estimate risk of disease for the population. Because the cases are a non-representative sample, propensity weighting is developed using the disease surveillance system. The risk model is evaluated using cross-validation, area under the ROC curve and quantification of risk stratification that take account of the propensity weighting and the sample design of the survey, These methods are illustrated for developing a prediction model of oropharyngeal cancer in the US population. The cases come from a clinical setting at Ohio State University, the population controls are from the National Health Examination Survey, and the National Cancer Institute Surveillance, Epidemiology, and End Results is used calibrate the cases. Methods for the model evaluation and variance estimation are discussed.

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

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