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Activity Number: 322 - Time-To-Event Models in Complex Observational Studies
Type: Invited
Date/Time: Tuesday, July 30, 2019 : 10:30 AM to 12:20 PM
Sponsor: Biometrics Section
Abstract #300114
Title: Validating Risk Prediction Models with Sub-Samples of Cohorts
Author(s): Ruth Pfeiffer* and Mitchell Henry Gail and Yei Eun Shin
Companies: National Cancer Institute and National Cancer Institute, Division of Cancer Epidemiology and Genetics and National Cancer Institute
Keywords: Efficient Weighting Methods; Calibration; Influence functions

When validating a risk model in an external cohort, often not all predictor variables in the model are available on all cohort members. Missingness can be random or by design (e.g. are available only in case-cohort and nested case-control samples). Weighting methods and imputation can address the missingness problem. We propose methods to improve weighting approaches. We first create a pseudorisk model using information from variables available for the entire cohort. Then we modify known sampling weights by survey calibration so that the weighted sum of the pseudo-risk in the complete data equals its cohort sum. We also study poststratification of the weights, based on pseudo-risk. We compared observed (O) to expected (E) counts in simulations to assess calibration and relative efficiencies of the methods, compared to analyses assuming complete exposure information. We also compare them to using multiple imputation by chained equations (MICE). In our simulations, all weighting and imputation procedures for O/E were unbiased. Survey calibration and poststratification were more efficient than standard weighting, and often more efficient than imputation.

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

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