This project proposed that given a basic model of the decisional logic driving stable adherence it might be possible to reliably anticipate increased vulnerability to discontinuation and use such a mode to design behavioral interventions that are specifically tailored to deliver course corrections that are temporally and contextually optimal. A logistic regression was coded and run using the core model predicted by the experts as a base for how the variables interact to make predictions. Then a logistic regression with stepwise selection was coded and run using the core model as a base for how the variables interact and to allow for variables to be added to make the prediction more accurate. The logistic regression with stepwise selection produced both an augmented core model and a de novo model. There were two nodes that had a variable added over 75% of the time to the augmented core. These nodes were health literacy and treatment fatigue. The accuracy for the core model, augmented core model, and de novo model were all accurate with respective overall accuracies being 63.5%, 66.3%, and 67.4%. However, it is important to note that we were able to outperform the expert model by doing the logistic regression with stepwise selection. The results show that the causal interaction diagram predicted by the experts was fairly accurate. These results then can be used to further the research needed to make a program that will help predict the chances of adherence and to be able help doctors work with the patients to stay adherent.

Library of Congress Subject Headings

Breast--Cancer--Patients--Rehabilitation--Research; Breast--Cancer--Hormone therapy; Patient compliance--Forecasting; Patient compliance--Mathematical models

Publication Date


Document Type


Student Type


Degree Name

Bioinformatics (MS)

Department, Program, or Center

Thomas H. Gosnell School of Life Sciences (COS)


Gary Skuse

Advisor/Committee Member

Gordon Broderick

Advisor/Committee Member

Matt Morris


RIT – Main Campus

Plan Codes