Tomasz Burzykowski

Design-based analysis of surveys: a bovine herpesvirus 1 case study

This paper critically assesses the design implications for the analysis of surveys of infections. It indicates the danger of not accounting for the study design in the statistical investigation of risk factors. A stratified design often implies an increased precision while clustering of infection results in a decreased precision. Through pseudo-likelihood estimation and linearisation of the variance estimator, the design effects can be taken into account in the analysis. The intra-cluster-correlation can be investigated through a logistic random effect model and a generalised estimating equation (GEE), allowing the investigation of the extent of spread of infections in a herd (cluster). The advantage of using adaptive Gaussian quadrature in a logistic random effect model is discussed. Applicable software is briefly reviewed. The methods are illustrated with data from a bovine herpesvirus 1 (BHV-1) serosurvey of Belgian cattle.

Classification of Trends in Dose-Response Microarray Experiments Using Information Theory Selection Methods~!2009-02-24~!2009-07-09~!2009-12-23~!

Dose-response microarray experiments consist of monitoring expression levels of thousands of genes with respect to increasing dose of the treatment under investigation. The primary goal of such an experiment is to establish a dose-response relationship, while the secondary goals are to determine the minimum effective dose level and to identify the shape of the dose-response curve. Recently, Lin et al. (1) discussed several testing procedures to test for monotone trend based on isotonic regression of the observed means (2,3). Once a monotone relationship between the gene expression and dose is established, there is a set of R possible monotone models that can be fitted to the data. A selection of the best model from this set allows us to identify both the shape of dose-response curve and the minimum effective dose level. In this paper we focus on classification of dose-response curve shapes using the information theory model selection. In particular, the Order Restricted Information Criterion (ORIC) is discussed for the inference under order restriction. The posterior probability of the model is calculated using information criteria that take into account both the goodness-of-fit and the complexity of the models. The method is applied to a dose-response microarray experiment with 12 arrays (for three samples at each of the four dose levels) with 16,998 genes.

Genomic biomarkers for a binary clinical outcome in early drug development microarray experiments.

In this article, we discuss methods to select three different types of genes (treatment related, response related, or both) and investigate whether they can serve as biomarkers for a binary outcome variable. We consider an extension of the joint model introduced by Lin et al. (2010) and Tilahun et al. (2010) for a continuous response. As the model has certain drawbacks in a binary setting, we also present a way to use classical selection methods to identify subgroups of genes, which are treatment and/or response related. We evaluate their potential to serve as biomarkers by applying DLDA to predict the response level.

A Meta-analytic Validation Framework for Continuous Outcomes

A meta-analytic approach was called for by several authors, e.g., Albert et al. (1998). A first formal proposal, using a Bayesian approach, was given by Daniels and Hughes (1997). Buyse et al. (2000a) extended these ideas using the theory of linear mixed-effects models. Gail et al. (2000) extended it further using generalized estimating equations methodology. In what follows, we describe the approach as proposed by Buyse et al. (2000a).