Inference for case-control studies with incident and prevalent cases

TL;DR

This paper introduces an efficient statistical method to estimate disease exposure associations in case-control studies that include both incident and prevalent cases, correcting for survival bias and improving estimation accuracy.

Contribution

It extends the exponential tilting model to handle two case groups and develops an empirical likelihood approach that accounts for survival bias and backward times.

Findings

Efficient estimates of odds ratios for disease incidence were obtained.

The method corrects for survival bias in prevalent cases.

Simulations show improved efficiency when combining incident and prevalent cases.

Abstract

We propose and study a fully efficient method to estimate associations of an exposure with disease incidence when both, incident cases and prevalent cases, i.e. individuals who were diagnosed with the disease at some prior time point and are alive at the time of sampling, are included in a case-control study. We extend the exponential tilting model for the relationship between exposure and case status to accommodate two case groups, and correct for the survival bias in the prevalent cases through a tilting term that depends on the parametric distribution of the backward time, i.e. the time from disease diagnosis to study enrollment. We construct an empirical likelihood that also incorporates the observed backward times for prevalent cases, obtain efficient estimates of odds ratio parameters that relate exposure to disease incidence and propose a likelihood ratio test for model parameters that has a standard chi-squared distribution. We quantify the changes in efficiency of association parameters when incident cases are supplemented with, or replaced by, prevalent cases in simulations. We illustrate our methods by estimating associations of single nucleotide polymorphisms (SNPs) with breast cancer incidence in a sample of controls and incident and prevalent cases from the U.S. Radiologic Technologists Health Study.