Affiliations: [a] Department of Neuroscience, University of California, San Diego, La Jolla, CA, USA | [b] Department of Family Preventive Medicine Division of Biostatistics, University of California, San Diego, La Jolla, CA, USA
Correspondence:
[*]
Correspondence to: Steven D. Edland, PhD, Associate Professor of Biostatistics, Associate Professor of Neuroscience, University of California, San Diego 9500 Gilman Dr. M/C 0948, La Jolla, CA 92093-0948. Tel.: +858 677 1550; Fax: +858 622 5845. E-mail: sedland@ucsd.edu.
Abstract: The Alzheimer research community is actively pursuing novel biomarker and other biologic measures to characterize disease progression or to use as outcome measures in clinical trials. One product of these efforts has been a large literature reporting power calculations and estimates of sample size for planning future clinical trials and cohort studies with longitudinal rate of change outcome measures. Sample size estimates reported in this literature vary greatly depending on a variety of factors, including the statistical methods and model assumptions used in their calculation. We review this literature and suggest standards for reporting power calculation results. Regardless of the statistical methods used, studies consistently find that volumetric neuroimaging measures of regions of interest, such as hippocampal volume, outperform global cognitive scales traditionally used in clinical treatment trials in terms of the number of subjects required to detect a fixed percentage slowing of the rate of change observed in demented and cognitively impaired populations. However, statistical methods, model assumptions, and parameter estimates used in power calculations are often not reported in sufficient detail to be of maximum utility. We review the factors that influence sample size estimates, and discuss outstanding issues relevant to planning longitudinal studies of Alzheimer's disease.
