Date of Award

4-2017

Degree Name

Doctor of Philosophy

Department

Statistics

First Advisor

Dr. Hyun Keun Cho

Second Advisor

Dr. Joseph McKean

Third Advisor

Dr. Magdalena Niewiadomska-Bugaj

Fourth Advisor

Dr. Jun-Seok Oh

Keywords

Subgrouping, Random Effects Linear Model, Validation, Growth Curve Model, Quadratic Inference Functions

Abstract

In clinical trials and biomedical studies, treatments are compared to determine which one is effective against illness. Growth curve analysis can be beneficial in longitudinal biomedical studies, as we can evaluate the treatment effect on the response over time. The generalized growth curve model using polynomial regression is proposed for longitudinal data. An optimal degree for the polynomial is obtained using the BIQIF, an adaptation of the Bayesian information criterion. Quadratic inference functions are used to estimate the parameters of the model, which takes into account the fact that repeated measurements from the same subject are more likely to be correlated. The equality of the growth curves is assessed using an asymptotically chi-square test statistic. Through this test, it could be shown that multiple treatments perform similarly, leading to the recommendation of either, however individuals can react to the same treatment very differently. A complete process for longitudinal data is also proposed that identifies subgroups of the population that would benefit from a specific treatment. A random effects linear model is used to evaluate individual treatment effects longitudinally where the random effects identify a positive or negative reaction to the treatment over. With the individual treatment effects and characteristics of the patients, various classifcation algorithms are applied to build prediction models for subgrouping. While many subgrouping approaches have been developed recently, most of them do not check its validity. As such, a simple validation approach is proposed which not only determines if the subgroups used are appropriate and beneficial, but also compares methods to predict individual treatment effects. All proposed methods are confirmed with simulation studies and analysis of data from the Women Entering Care study on depression.

Access Setting

Dissertation-Open Access

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