A method for exploratory repeated-measures analysis applied to a breast-cancer screening study

TL;DR

This paper introduces a comparative method for exploratory repeated-measures analysis that enhances understanding of individual variability and performance differences in breast-cancer screening data, addressing limitations of separate model fitting.

Contribution

It presents a novel approach that complements point estimates with analysis of estimation uncertainty, aiding in identifying unusual behavior and informing random-effects modeling.

Findings

Flagged unusual reader behavior

Assessed differences in performance

Identified potential random-effects models

Abstract

When a model may be fitted separately to each individual statistical unit, inspection of the point estimates may help the statistician to understand between-individual variability and to identify possible relationships. However, some information will be lost in such an approach because estimation uncertainty is disregarded. We present a comparative method for exploratory repeated-measures analysis to complement the point estimates that was motivated by and is demonstrated by analysis of data from the CADET II breast-cancer screening study. The approach helped to flag up some unusual reader behavior, to assess differences in performance, and to identify potential random-effects models for further analysis.