A new weighting method when not all the events are selected as cases in a nested case-control study

TL;DR

This paper introduces a new weighting method for inverse probability weighted estimation in nested case-control studies where only a subset of events are selected as cases, improving efficiency when not all events are used.

Contribution

It proposes a novel weighting scheme and variance estimation method for NCC studies with partial event sampling, enhancing estimation accuracy and efficiency.

Findings

New weights outperform previous methods in efficiency.

Perturbation method accurately estimates variance.

Method demonstrated with Framingham cohort data.

Abstract

Nested case-control (NCC) is a sampling method widely used for developing and evaluating risk models with expensive biomarkers on large prospective cohort studies. The biomarker values are typically obtained on a sub-cohort, consisting of all the events and a subset of non-events. However, when the number of events is not small, it might not be affordable to measure the biomarkers on all of them. Due to the costs and limited availability of bio-specimens, only a subset of events is selected to the sub-cohort as cases. For these "untypical" NCC studies, we propose a new weighting method for the inverse probability weighted (IPW) estimation. We also design a perturbation method to estimate the variance of the IPW estimator with our new weights. It accounts for between-subject correlations induced by the sampling processes for both cases and controls through perturbing their sampling indicator variables, and thus, captures all the variations. Furthermore, we demonstrate, analytically and numerically, that when cases consist of only a subset of events, our new weight produces more efficient IPW estimators than the weight proposed in Samuelsen (1997) for a standard NCC design. We illustrate the estimating procedure with a study that aims to evaluate a biomarker-based risk prediction model using the Framingham cohort study.