TITLE: Statistical Challenges in the Analysis of Biomarker Data
SPEAKERS: Professor Stephen W. Looney, Augusta University
The statistical analysis of biomarker data can be very challenging. Sample sizes can be quite small and biomarker data often exhibit violations of many of the assumptions underlying standard statistical analyses. Biomarker data are often skewed, and there may be issues with discordant observations. In this presentation, we describe several of the challenges that one is likely to encounter in the routine analysis of biomarker data and offer recommendations on methods for dealing with these challenges. For example, we discuss methods for (1) detecting and accommodating violations of underlying assumptions, (2) analyzing clustered and correlated data, and (3) detecting and accommodating outliers. Each of these challenges is illustrated using one or more examples taken from published biomarker studies. Recommendations are also provided on software for implementing the recommended methods of analysis.