Tree models for covariate-dependent method agreement with repeated measurements in clinical research

2026-03-31

AI Summary

Traditional Bland-Altman analysis assumes homogeneous agreement between measurement methods across all individuals, rendering it ill-suited for handling heterogeneity induced by covariates or repeated measurements. This work proposes the Conditional Agreement Tree (COAT), which extends regression tree methodology to repeated-measures settings by modeling how both the mean and variance of method differences depend on covariates and decomposing these into subject-specific components. COAT explicitly identifies subgroups exhibiting heterogeneous agreement patterns. Monte Carlo simulations demonstrate that COAT effectively controls Type I error rates in finite samples and gains statistical power with increasing sample size. Applied to a comparison of cardiac output devices, COAT successfully uncovers significant influences of patient characteristics on measurement agreement.

Abstract

Background: In clinical research, the Bland-Altman analysis is commonly used to assess agreement of metric measurements made by two or more techniques, devices or methods. The approach can also deal with repeated measurements per subject or observational unit. However, a strong and implicit assumption is that agreement of methods is homogeneous across subjects. Objective: To extend the previously introduced multivariable modeling of conditional method agreement with single measurements per subject to the frequent case of repeated measurements. Methods: Appropriate regression trees, called conditional method agreement trees (COAT), are generalized to capture the dependence of the parameters of the Bland-Altman analysis on covariates. These parameters, the expectation and variance of the differences between the methods, are decomposed into subject-specific components to account for repeated measurements. Whilst the theoretical, asymptotic properties of tree models are known, a simulation study was carried out to assess the performance of COAT in finite samples. A comparison of devices measuring cardiac output serves as an application example. Results: COAT is applicable to the two relevant cases of paired and unpaired repeated measurements. In the simulation study, it controlled the type-I error at the nominal level and could detect covariate-dependent method agreement with increasing sample size. The Adjusted Rand Index, a measure of concordance between the estimated and true subgroups, reached very high values close to the maximum of 1. The analysis of cardiac output showed that patients' characteristics may influence the agreement between measuring devices, with implications for use in patient care. Conclusion: COAT can explicitly define subgroups of heterogeneous method agreement in dependence of covariates with appropriate statistical testing in case of repeated measurements.

Problem

• method agreement
• repeated measurements
• covariate-dependent
• Bland-Altman analysis
• heterogeneous agreement

Innovation

• conditional method agreement
• repeated measurements
• regression trees
• Bland-Altman analysis
• covariate-dependent heterogeneity