  Method comparison studies explore the agreement of measurements made by two
or more methods. Commonly, agreement is evaluated by the well-established
Bland-Altman analysis. However, the underlying assumption is that differences
between measurements are identically distributed for all observational units
and in all application settings. We introduce the concept of conditional method
agreement and propose a respective modeling approach to alleviate this
constraint. Therefore, the Bland-Altman analysis is embedded in the framework
of recursive partitioning to explicitly define subgroups with heterogeneous
agreement in dependence of covariates in an exploratory analysis. Three
different modeling approaches, conditional inference trees with an appropriate
transformation of the modeled differences (CTreeTrafo), distributional
regression trees (DistTree), and model-based trees (MOB) are considered. The
performance of these models is evaluated in terms of type-I error probability
and power in several simulation studies. Further, the adjusted rand index (ARI)
is used to quantify the models' ability to uncover given subgroups. An
application example to real data of accelerometer device measurements is used
to demonstrate the applicability. Additionally, a two-sample Bland-Altman test
is proposed for exploratory or confirmatory hypothesis testing of differences
in agreement between subgroups. Results indicate that all models were able to
detect given subgroups with high accuracy as the sample size increased.
Relevant covariates that may affect agreement could be detected in the
application to accelerometer data. We conclude that conditional method
agreement trees (COAT) enable the exploratory analysis of method agreement in
dependence of covariates and the respective exploratory or confirmatory
hypothesis testing of group differences. It is made publicly available through
the R package coat.
