Online Program Home
My Program

Abstract Details

Activity Number: 83 - SPEED: Survival Analysis
Type: Contributed
Date/Time: Sunday, July 29, 2018 : 4:00 PM to 4:45 PM
Sponsor: Biometrics Section
Abstract #333011
Title: Estimation in the Nested Case-Control Design Under Model Misspecification
Author(s): Michelle Nuno* and Daniel L. Gillen
Companies: and University of California, Irvine
Keywords: efficient sampling; model misspecification; partial likelihood; nested case-control design; non-proportional hazards

Efficient sampling designs such as the nested case-control design help decrease study costs and burden on participants when the event of interest is rare and the covariate of interest is difficult or expensive to measure. By collecting full covariate information on all subjects who experience an event and only a subsample of those who do not experience an event, the nested case-control design sampling scheme leads to an estimator that can be magnitudes more efficient than a simple random sample from the full cohort when the model is specified correctly. In this presentation, we show that under model misspecification, the estimand for the nested case-control design depends on the censoring distribution and on the number of controls selected, leading to potentially unreproducible results depending upon logistical design decisions. We propose a new estimator that applies frequency weights to recover the full cohort estimator and reweights the contribution of each subject to the partial likelihood based on a covariate-dependent inverse probability of censoring estimate. Theoretical and empirical results are presented to illustrate the utility of the proposed estimator.

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

Back to the full JSM 2018 program