Journal of Biometrics & Biostatistics

Two crucial problems arise from a microarray experiment in which the primary objective is to locate differentially expressed genes for the diagnosis of diseases such as cancer and Alzheimer’s. The first problem is the detection of a subset of genes which provides an optimum discriminatory power between diseased and normal subjects, and the second problem is the statistical estimation of discriminatory power from the optimum subset of genes between two groups of subjects. We develop a new method to select an optimum subset of discriminatory genes by searching over possible linear combinations of gene expression profiles and locating the one which provides the maximum discriminatory power between two sources of RNA as measured by the area under the receiver operating characteristic

Clustered Poisson data frequently appear in medical research. Interest often focuses on examination of an exposure effect within clusters. The objective of this paper is to compare the performance of six methods for estimating the exposure effect for clustered Poisson data: 1) independent Poisson; 2) fixed cluster effects Poisson; 3) conditional likelihood Poisson estimation; 4) Generalized Estimating Equations (GEE); 5) random cluster effects Poisson; and 6) random cluster effects Poisson, with separate between- and within-cluster effects. Biases and standard errors of within- cluster exposure effects are compared across the six statistical methods considering constant or varying exposure ratio