title: Options and Considerations for Adaptive Laboratory Experiments.
abstract: Motivated by laboratory experiments that fail to reach significance, we developed a small sample size approach to designing a subsequent experiment that controls overall type I error and achieves sufficient conditional power. We focus on experiments with leukemia cells, and use a specific example in Chronic Lymphocytic Leukemia to discuss unanticipated patient variance and difficult to predict interaction effect sizes. We emphasize the importance of achieving significance in the first run of an experiment, which results in simplifying the multiple considerations usually associated with interim analysis and decision making in adaptive clinical trials. Within the context of combination testing for an adaptive laboratory experiment, we show that a range of reasonable options for the futility cut-off, effect size estimation, and significance level for the first run provide similar power and expected overall sample size. We contrast this approach to a naive procedure in which a second unplanned experiment is run based on non-significance in the first experiment, and data are combined as if they were obtained from one run.
authors.affiliation: Center for Biostatistics, The Ohio State University, 2012 Kenny Road, Columbus, OH 43221, U.S.A.
authors.forename: Lai
authors.initials: L
authors.lastname: Wei
authors.affiliation: Center for Biostatistics, The Ohio State University, 2012 Kenny Road, Columbus, OH 43221, U.S.A.
authors.forename: David
authors.initials: D
authors.lastname: Jarjoura
keywords: Conditional error function; Conditional power; Sample size re-estimation; Small sample size