Distance-based Classification and Regression Trees for the analysis of complex predictors in health and medical research

Abstract

Predicting patient outcomes based on patient characteristics and care processes is a common task in medical research. Such predictive features are often multifaceted and complex, and are usually simplified into one or more scalar variables to facilitate statistical analysis. This process, while necessary, results in a loss of important clinical detail. While this loss may be prevented by using distance-based predictive methods which better represent complex healthcare features, the statistical literature on such methods is limited, and the range of tools facilitating distance-based analysis is substantially smaller than those of other methods. Consequently, medical researchers must choose to either reduce complex predictive features to scalar variables to facilitate analysis, or instead use a limited number of distance-based predictive methods which may not fulfil the needs of the analysis problem at hand. We address this limitation by developing a Distance-Based extension of Classification and Regression Trees (DB-CART) capable of making distance-based predictions of categorical, ordinal and numeric patient outcomes. We also demonstrate how this extension is compatible with other extensions to CART, including a recently published method for predicting care trajectories in chronic disease. We demonstrate DB-CART by using it to expand upon previously published dose–response analysis of stroke rehabilitation data. Our method identified additional detail not captured by the previously published analysis, reinforcing previous conclusions. We also demonstrate how by combining DB-CART with other extensions to CART, the method is capable of making predictions about complex, multifaceted outcome data based on complex, multifaceted predictive features.