Comparison of the Efficiency of Robust Estimators for the Nested Case-Control Design

3645 

Contributed Abstract 

Paper 

Michelle Nuno (1)

(1) University of Southern California, Los Angeles, CA

Michelle Nuno  
University of Southern California

Michelle Nuno  
N/A

The nested case-control (NCC) design is often used to reduce data collection costs in rare disease settings where resources are limited. The NCC sampling scheme randomly samples a small number of controls from the risk set at each event time. Samuelsen (1997) proposed a pseudolikelihood-based approach for estimation of model coefficients when using the NCC sampling scheme. This estimator allows sampled controls to enter all risk sets for which they are at risk and reweights contributions to account for biased sampling. Nuño and Gillen (2022) found that under model misspecification, the standard estimator proposed by Thomas (1977) estimates a different quantity that depends on the number of controls sampled at each event time. To account for this, they proposed an estimator based on the missing data framework, which imputes covariate values of subjects at risk in the full cohort who are not sampled under the NCC design. The Sameulsen estimator, while not developed for this purpose, also has this benefit. The current work compares the efficiency of the two estimators under various settings and provides considerations for the use of each estimator.

nested case-control design|efficient sampling designs|time-to-event data| | |

Biometrics Section

Survival Analysis