Date on Master's Thesis/Doctoral Dissertation

12-2014

Document Type

Doctoral Dissertation

Degree Name

Ph. D.

Department

Bioinformatics and Biostatistics

Committee Chair

Rai, Shesh Nath

Committee Co-Chair (if applicable)

Baumgartner, Kathy B.

Committee Member

Brock, Guy N.

Committee Member

Cooper, Nigel G.F.

Committee Member

Wu, Dongfeng

Author's Keywords

Classification; Machine learning; Dimension reduction; Interaction; Melanoma; Clinical study

Subject

Patients--Classification--Mathematical models; Medical informatics; Cancer--Treatment

Abstract

It is widely recognized that many cancer therapies are effective only for a subset of patients. However clinical studies are most often powered to detect an overall treatment effect. To address this issue, classification methods are increasingly being used to predict a subset of patients which respond differently to treatment. This study begins with a brief history of classification methods with an emphasis on applications involving melanoma. Nonparametric methods suitable for predicting subsets of patients responding differently to treatment are then reviewed. Each method has different ways of incorporating continuous, categorical, clinical and high-throughput covariates. For nonparametric and parametric methods, distance measures specific to the method are used to make classification decisions. Approaches are outlined which employ these distances to measure treatment interactions and predict patients more sensitive to treatment. Simulations are also carried out to examine empirical power of some of these classification methods in an adaptive signature design. Results were compared with logistic regression models. It was found that parametric and nonparametric methods performed reasonably well. Relative performance of the methods depends on the simulation scenario. Finally a method was developed to evaluate power and sample size needed for an adaptive signature design in order to predict the subset of patients sensitive to treatment. It is hoped that this study will stimulate more development of nonparametric and parametric methods to predict subsets of patients responding differently to treatment.

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