Optimizing Quantitative Trait Outcome Measures for Clinical Trials of Chronic Progressive Disease
M. Colin Ard, U. California San Diego *Steve Edland, University of California, San Diego Keywords: Outcome measures, composite scores, Item response theory, Alzheimer's disease Statistical methods can be remarkably effective at improving the performance of quantitative trait outcome measures used in clinical trials of chronic progressive disease. We illustrate by way of example two applications of statistical methods to optimize performance of outcome measures. The first example uses item response theory to rescaled a commonly applied outcome measure used in Alzheimer's disease treatment trials and reduce sample size requirements by 15% or more. The second example uses generalized least squares methods to combine multiple candidate outcome measures into an optimally efficient single "composite" score. These examples illustrate the potential value that can be added by considered application of statistical methods to instrument development.
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Key Dates
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April 30 - May 22, 2013
Invited Abstract Submission Open -
June 4, 2013
Online Registration Opens -
August 9 - August 23, 2013
Invited Abstract Editing -
August 23, 2013
Short Course materials due from Instructors -
August 26, 2013
Housing Deadline -
September 9, 2013
Cancellation Deadline and Registration Closes @ 11:59 pm EDT -
September 16 - September 18, 2013
Marriott Wardman Park, Washington, DC