Classification of clinical outcomes using high-throughput informatics: Part 1 – nonparametric method reviews

Article type: Research Article

Authors: Cambon, A.C.^{a; d} | Baumgartner, K.B.^b | Brock, G.N.^a | Cooper, N.G.F.^c | Wu, D.^a | Rai, S.N.^{a; d; *}

Affiliations: [a] Department of Bioinformatics and Biostatistics, University of Louisville, Louisville, KY, USA | [b] Department of Epidemiology and Population Health, University of Louisville, Louisville, KY, USA | [c] Department of Anatomical Sciences {and} Neurobiology, University of Louisville, Louisville, KY, USA | [d] Biostatistics Shared Facility, James Graham Brown Cancer Center, University of Louisville, Louisville, KY, USA

Correspondence: [*] Corresponding author: Shesh N. Rai, Biostatistics Shared Facility, James Graham Brown Cancer Center, University of Louisville, 505 South Hancock Street, Room 211, Louisville, KY 40202, USA. Tel.: +1 502 852 4030; Fax: +1 502 852 7979; E-mail: Shesh.Rai@Louisville.Edu.

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. More recent methods have built-in dimension reduction methods for high throughput data. Pre-validation is one method of assessing the added value of high-throughput data to clinical covariates. The way in which treatment interactions are incorporated is important if the goal is to predict a subset of patients which respond differently to treatment. For nonparametric methods, distance measures specific to the method are used to make classification decisions. Approaches are outlined which employ these distances to measure treatment interactions. It is hoped that this study will stimulate more development of nonparametric methods to predict subsets of patients responding differently to treatment.

Keywords: Classification, machine learning, dimension reduction, interaction, melanoma, clinical study

DOI: 10.3233/MAS-140310

Journal: Model Assisted Statistics and Applications, vol. 10, no. 1, pp. 3-23, 2015