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Inference for case-control studies with incident and prevalent cases.

Marlena Maziarz¹, Yukun Liu², Jing Qin³

¹National Cancer Institute, National Institutes of Health, Rockville, Maryland.

|January 17, 2019

Summary

This study introduces an efficient method for case-control studies to estimate exposure-disease associations using both incident and prevalent cases. The approach corrects for survival bias in prevalent cases, improving accuracy in epidemiological research.

Keywords:

density ratio model empirical likelihood exponential tilting model length biased sampling outcome dependent sampling survival bias

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Area of Science:

Epidemiology
Biostatistics
Genetic Epidemiology

Background:

Case-control studies are crucial for disease etiology research.
Including both incident and prevalent cases can increase study power but introduces survival bias.
Accurate estimation of exposure-disease associations is vital for public health.

Purpose of the Study:

To develop an efficient statistical method for estimating exposure-disease incidence associations in case-control studies with both incident and prevalent cases.
To address and correct for survival bias inherent in prevalent case data.
To provide a robust statistical framework for analyzing complex case-control data.

Main Methods:

Extension of the exponential tilting model to incorporate two case groups (incident and prevalent).
Development of a tilting term to adjust for survival bias based on backward time distribution.
Construction of an empirical likelihood incorporating observed backward times for prevalent cases.
Proposal of a likelihood ratio test with a standard chi-squared distribution for parameter testing.

Main Results:

Efficient estimation of odds ratio parameters relating exposure to disease incidence.
Quantification of efficiency changes when prevalent cases supplement or replace incident cases via simulations.
Demonstration of the method's applicability in a real-world genetic epidemiology study.

Conclusions:

The proposed method provides efficient and unbiased estimates of exposure-disease associations in case-control studies with mixed case ascertainment.
The approach effectively corrects for survival bias, enhancing the reliability of findings from prevalent cases.
This statistical framework offers a valuable tool for genetic association studies and other epidemiological research.