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Emerg Themes Epidemiol. 2017 Sep 20;14:11. doi: 10.1186/s12982-017-0064-4. eCollection 2017.

Decision trees in epidemiological research.

Author information

1
Urban Big Data Centre, University of Glasgow, 7 Lilybank Gardens, Glasgow, G12 8RZ UK.
2
Division of Biostatistics, University of Minnesota, Twin Cities, A453 Mayo Building, MMC 303, 420 Delaware St SE, Minneapolis, MN 55455 USA.
3
Division of Epidemiology and Community Health, University of Minnesota, Twin Cities, West Bank Office Building, 1300 South Second St, Suite 300, Minneapolis, MN 55454 USA.
4
Division of Applied Research, Allina Health, 2925 Chicago Ave, Minneapolis, MN 55407 USA.

Abstract

BACKGROUND:

In many studies, it is of interest to identify population subgroups that are relatively homogeneous with respect to an outcome. The nature of these subgroups can provide insight into effect mechanisms and suggest targets for tailored interventions. However, identifying relevant subgroups can be challenging with standard statistical methods.

MAIN TEXT:

We review the literature on decision trees, a family of techniques for partitioning the population, on the basis of covariates, into distinct subgroups who share similar values of an outcome variable. We compare two decision tree methods, the popular Classification and Regression tree (CART) technique and the newer Conditional Inference tree (CTree) technique, assessing their performance in a simulation study and using data from the Box Lunch Study, a randomized controlled trial of a portion size intervention. Both CART and CTree identify homogeneous population subgroups and offer improved prediction accuracy relative to regression-based approaches when subgroups are truly present in the data. An important distinction between CART and CTree is that the latter uses a formal statistical hypothesis testing framework in building decision trees, which simplifies the process of identifying and interpreting the final tree model. We also introduce a novel way to visualize the subgroups defined by decision trees. Our novel graphical visualization provides a more scientifically meaningful characterization of the subgroups identified by decision trees.

CONCLUSIONS:

Decision trees are a useful tool for identifying homogeneous subgroups defined by combinations of individual characteristics. While all decision tree techniques generate subgroups, we advocate the use of the newer CTree technique due to its simplicity and ease of interpretation.

KEYWORDS:

Decision trees; Predictors; Subgroup heterogeneity

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