Workshop Instructors:

Edward L. Spitznagel, Ph.D., Professor of Mathematics & Biostatistics, Washington University. Over the past 15 years, Ed has provided statistical consulting in areas such as pharmacokinetic and pharmacodynamic studies of generic and pioneer drugs for major pharmaceutical companies. Ed was a 2002 recipient of the Outstanding Teacher of the Year awarded by the Mathematical Association of America.

Tom Downey, M.S., President, Partek Incorporated. Tom provides statistical consulting to pharmaceutical and biotech companies regarding the analysis of life science data, and has given many workshops on experiment design and statistical analysis of microarray data.

Over the past 5 years, Ed and Tom have worked together on the design and analysis of several dozen Affymetrix-based experiments at laboratories around the world and will share what they have learned from these studies with the attendees of this workshop.

Microarray data contains treatment and/or phenotype effects embedded in a sea of technical and biological noise. This workshop will demonstrate how to use proven statistical methods of experiment design and data analysis to reliably identify biological effects of interest while controlling and removing noise due to biological and technical nuisance effects. Attendees will learn how to employ completely randomized block designs to isolate and remove batch effects due to processing batches, etc. clearly revealing the signals from the biological factors of interest. In addition to p-values, estimates of ratios and fold-changes will be examined from a statistical perspective. The techniques will be demonstrated using published Affymetrix gene expression and exon experiments. Attendees will learn how to apply and interpret statistical techniques such as analysis of variance (ANOVA) - including mixed linear models and linear contrasts, multiple test corrections, principal components analysis, and multidimensional scaling.

Today’s Affymetrix GeneChip^TM technologies offer a potentially valuable tool to aid oncologists in choosing the most effective treatments for cancer patients. In many types of cancer, the response to treatment varies widely from patient to patient. It is believed that genetic and/or genomic “biomarkers” may be useful in identifying which cancer patients are likely to benefit from a particular treatment. We will use data from recently published studies using Affymetrix GeneChip technology to demonstrate statistical tests that use cancer recurrence rates and survival times to identify biomarkers that are predictive of patient outcome. These biomarkers are then used to build and estimate the accuracy of class prediction models to identify which patients are likely to respond to a particular treatment. Attendees will learn how to perform and interpret statistical techniques such as Cox regression, Kaplan-Meier, classification, cross-validation, and bootstrap.

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Early Bird Registration Fees (on or before April 30):
$345 for Academic/Government/Hospital
$445 for Industrial/Pharmaceutical

Regular Registration Fees (after April 30):
$395 for Academic/Government/Hospital
$495 for Industrial/Pharmaceutical

Register Now  (Seating is Limited)

The workshop will be held at the Fairmont San Francisco. Breakfast and lunch will be provided for all registered attendees.