Overall Power Calculation for High-Dimensional Design

• Activity Number: 368
• Type: Contributed
• Date/Time: Tuesday, August 6, 2013 : 10:30 AM to 12:20 PM
• Sponsor: Biometrics Section
• Author(s): Yueh-Yun Chi*+ and Matthew J. Gribbin and Jacqueline J Johnson and Keith E. Muller
• Companies: University of Florida and MedImmune and The University of North Carolina and University of Florida
• Keywords: MANOVA ; Metabolomics ; Genomics ; Proteomics

Abstract

The complexity of system biology means that any metabolic, genetic, or proteomic pathway typically includes so many components (e.g., molecules) that statistical methods specialized for overall testing of high dimensional and commensurate outcomes are required. We develop accurate power and sample size methods to facilitate study planning for high dimensional pathway analysis. With an account of any complex correlation structure between high dimensional outcomes, we derive a minimum set of constants and parameters sufficient and practical for power calculation. Simulations of group comparisons support the accuracy of the approximations even when the ratio of number of variables to sample size is large. Using the new methods and specifying the minimum set to determine power for a study of metabolic consequences of vitamin B6 deficiency helps illustrate the practical value of the new results. Free software implementing the new power and sample size methods applies to a wide range of designs, including one group pre- and post-intervention comparisons, multiple parallel group comparisons with one-way or factorial designs, and the adjustment and evaluation of covariate effects.