Design-analysis mismatch in the analysis of longitudinal HER2 data in breast cancer.

Abstract

This graphical abstract presents findings from a simulation study examining the impact of within-patient correlation on statistical inference in analyses of repeated binary clinical outcomes. Under conditions of strong intracluster correlation (200 patients, 10 observations per patient, 1000 simulated data sets), naive logistic regression that assumed independence substantially underestimated standard errors and led to marked inflation of the type I error rate (30.5%). In contrast, generalized estimating equations (GEE), which appropriately account for within-patient clustering, preserved nominal error control (5.7%). These results underscore the importance of using correlation-adjusted analytic approaches to ensure valid inference when evaluating patient-level exposures in longitudinal or clustered oncology data.