A Statistical Appraisal of Biomarker Selection Methods Applicable to HIV/AIDS Research

Abstract The past decade has seen an explosion in the availability and use of biomarkers data as a result of innovative discoveries and recent development of new biological and molecular techniques. Biomarkers are essential for at least four key purposes in biomedical research and public health practice: they are used for disease detection, diagnosis, prognosis, to identify patients who are most likely to benefit from selected therapies, and to guide clinical decision making. Determining the predictive and diagnostic value of biomarkers, singly and in combination, is essential to their being used effectively, and this has spurred the development of new statistical methodologies to assess the relationship between biomarkers and clinical outcomes. In this paper, we review both standard and novel statistical methods used for biomarker selection. We focus on techniques that could be readily applied to HIV infection research. Particular attention is given to deriving variable importance measures based on doubly robust methods such as targeted maximum likelihood estimation. We conclude by providing an example of the application of three novel techniques to a dataset from the Hormonal Contraception and HIV Genital Shedding and Disease Progression Study (GS Study) to select, among many candidate biomarkers, the best subset that is significantly associated with a CD4 cell decline to less than 350 cells/mm 3 .