Characterization and reduction of variability in selection based on effect-size using association measures in cohort study of heterogeneous diseases

TL;DR

This paper develops a methodology to characterize and reduce bias in association measures used in cohort studies of heterogeneous diseases, introducing a new measure with minimal bias for more reliable disease network analysis.

Contribution

It introduces a stochastic model-based association measure that reduces bias compared to standard measures like relative risk and phi correlation in disease cohort analysis.

Findings

The new measure exhibits the least overall bias among tested measures.

Application to a large cohort demonstrates improved robustness in disease association ranking.

The methodology aids in more accurate disease network characterization.

Abstract

Cohort studies employ pairwise measures of association to quantify dependencies among conditions and exposures. To reliably use these measures to draw conclusions about the underlying association strengths requires that the measures be robust and unbiased. These considerations assume greater significance when applied to disease networks, where associations among heterogeneous pairs of diseases are ranked. Using disease diagnoses data from a large cohort of 5.5 million individuals, we develop a comprehensive methodology to characterize the bias of standard association measures like relative risk and $\phi$ correlation. To overcome these biases, we devise a novel measure based on a stochastic model for disease development. The new measure is demonstrated to have the least overall bias and hence would be most suitable for application to heterogeneous disease cohorts.