A case study found that a regression tree outperformed multiple linear regression in predicting the relationship between impairments and Social and Productive Activities scores.

Objective: Many important physiologic and clinical predictors are continuous. Clinical investigators and epidemiologists' interest in these predictors lies, in part, in the risk they pose for adverse outcomes, which may be continuous as well. The relationship between continuous predictors and a continuous outcome may be complex and difficult to interpret. Therefore, methods to detect levels of a predictor variable that predict the outcome and determine the threshold for clinical intervention would provide a beneficial tool for clinical investigators and epidemiologists.

Study Design And Setting: We present a case study using regression tree methodology to predict Social and Productive Activities score at 3 years using five modifiable impairments. The predictive ability of regression tree methodology was compared with multiple linear regression using two independent data sets, one for development and one for validation.

Results: The regression tree approach and the multiple linear regression model provided similar fit (model deviances) on the development cohort. In the validation cohort, the deviance of the multiple linear regression model was 31% greater than the regression tree approach.

Conclusion: Regression tree analysis developed a better model of impairments predicting Social and Productive Activities score that may be more easily applied in research settings than multiple linear regression alone.