Date of Award
Spring 4-22-2026
Embargo Period
4-30-2028
Document Type
Dissertation
Degree Name
Doctor of Philosophy (PhD)
Department
Public Health Sciences
College
College of Graduate Studies
First Advisor
Bethany Wolf
Second Advisor
Paul Nietert
Third Advisor
Diane Kamen
Fourth Advisor
Jim Oates
Fifth Advisor
Jaime Speiser
Sixth Advisor
Jonathan Beall
Abstract
Dichotomization of continuous predictors to predict binary outcomes is a prevalent practice in clinical settings, utilized for patient diagnosis, prognosis, and resource allocation. In order to make decisions that are binary in nature, clinicians need binary interpretations on continuous measures. Despite its widespread use in clinical studies, dichotomizing continuous predictors to discriminate binary outcomes has faced substantial criticism since it can lead to a loss of valuable information and statistical power. Given the prevalent use of dichotomization in clinical decision making, outright dismissal based solely on the loss of information and power might not be the most prudent approach. There are scenarios where the information loss using a dichotomized version of a continuous predictor in regression estimation is not greater than using the original continuous version of the predictor.
By extending the model to incorporate multiple predictors, developing clinical prediction models often involves balancing uncertainty in the prediction of patient outcomes with the desire for interpretable, actionable models. Incorporating prior clinical knowledge and existing evidence when constructing these models can improve prediction accuracy and enhance clinical relevance. Traditional decision tree models provide an intuitive and interpretable approach for clinical research; however, they do not have an inherent mechanism to capture uncertainty in predictions. While methods based on sampling variability, such as bootstrapping or binomial confidence intervals, can be used with traditional decision trees to understand the uncertainty around predictions, these methods only approximate sampling variability rather than integrating uncertainty across parameters and possible model configurations. To address the limitations in traditional decision tree approaches, we develop a Bayesian decision tree approach to quantify parameter and structural uncertainty through a coherent probabilistic framework and to allow for integration of prior knowledge. The proposed approaches, known as BayLeaf-Reg and BayLeaf-Bin, provides probabilistic inference on the tree structure by estimating posterior distributions of variables being split on and their cut-points, yields posterior summaries of predictor importance and predicted outcome probabilities, and does not suffer from the same weak learner tendencies as traditional decision trees.
Recommended Citation
Keller, Everette, "Bayesian Hierarchical Model to Determine if Dichotomization is Justified with Extensions to Tree-Based Models" (2026). MUSC Theses and Dissertations. 1135.
https://medica-musc.researchcommons.org/theses/1135
Rights
Copyright is held by the author. All rights reserved.