ABSTRACT
We begin by arguing that the often used algorithm for the discovery and use of disease risk factors, stepwise logistic regression, is unstable. We then argue that there are other algorithms available that are much more stable and reliable (e.g. the lasso and gradient boosting). We then propose a protocol for the discovery and use of risk factors using lasso or boosting variable selection. We then illustrate the use of the protocol with a set of prostate cancer data and show that it recovers known risk factors. Finally, we use the protocol to identify new and important SNP based risk factors for prostate cancer and further seek evidence for or against the hypothesis of an anticancer function for Selenium in prostate cancer. We find that the anticancer effect may depend on the SNP-SNP interaction and, in particular, which alleles are present.
Subject(s)
Alleles , Prostatic Neoplasms/genetics , Prostatic Neoplasms/pathology , Selenium/metabolism , Algorithms , Antineoplastic Agents/pharmacology , Humans , Logistic Models , Male , Prostatic Neoplasms/drug therapy , Risk FactorsABSTRACT
Outlier detection can be very important in analyzing data from Scatchard plots. In this study, a robust (outlier-resistant) regression procedure was used in conjunction with a Scatchard plot to study the binding of the methylphenazinium cation with double-stranded DNA. The procedures, their results, and their advantages are discussed.
