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

TL;DR

This paper introduces COAT, a regression tree method that models covariate-dependent method agreement in repeated measurements, improving understanding of device agreement variability in clinical research.

Contribution

The paper extends multivariable modeling of method agreement to handle repeated measurements using regression trees, capturing covariate effects on agreement parameters.

Findings

COAT controls type-I error and detects covariate-dependent agreement with increasing sample size.

High concordance between estimated and true subgroups in simulation studies.

Application to cardiac output measurements shows patient characteristics influence device 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.