Reconciling Binary Replicates: Beyond the Average

TL;DR

This paper explores alternative scoring methods for binary replicate data, proposing median, penalized likelihood, and Bayesian approaches that outperform simple averaging, especially in medical diagnostics.

Contribution

It introduces and compares three novel methods for binary replicate analysis, addressing limitations of averaging and enhancing diagnostic accuracy.

Findings

Bayesian method outperforms averaging in simulations

Proposed methods improve disease prevalence estimation

Bayesian approach provides credible intervals for uncertainty

Abstract

Binary observations are often repeated to improve data quality, creating technical replicates. Several scoring methods are commonly used to infer the actual individual state and obtain a probability for each state. The common practice of averaging replicates has limitations, and alternative methods for scoring and classifying individuals are proposed. Additionally, an indecisive response might be wiser than classifying all individuals based on their replicates in the medical context, where 1 indicates a particular health condition. Building on the inherent limitations of the averaging approach, three alternative methods are examined: the median, maximum penalized likelihood estimation, and a Bayesian algorithm. The theoretical analysis suggests that the proposed alternatives outperform the averaging approach, especially the Bayesian method, which incorporates uncertainty and provides credible intervals. Simulations and real-world medical datasets are used to demonstrate the practical implications of these methods for improving diagnostic accuracy and disease prevalence estimation.