Department of Biostatistics Seminar/Workshop Series

T-Test on Fold Changes

Tatsuki Koyama, PhD

Assistant Professor of Biostatistics, Cancer Biostatistics Center, Vanderbilt-Ingram Cancer Center

Wednesday, June 16, 1:30-2:30pm, MRBIII Conference Room 1220

Basic science experiments often use a separate control group for each treatment group. Typically, the treatment group outcomes are scaled by the average of the corresponding control group outcomes. Despite its overwhelming popularity, this "fold change" method has serious statistical problems resulting in reduced validity. When the implicit variability of the control group outcomes is ignored, a large type I error inflation can result. Likewise, this scaling induces correlation and can substantially inflate the type I error when this correlation is ignored. We present simulations showing that this inflation results in type I error rates as high as 50% in everyday settings. We propose some computational and analytical approaches for dealing with this problem, and we present some practical recommendations for experimental designs with small sample sizes. Intended audience: Clinical and basic science researchers and statisticians.
Topic revision: r5 - 26 Apr 2013, JohnBock

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