JSM 2011 Online Program

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Abstract Details

Activity Number: 627
Type: Contributed
Date/Time: Thursday, August 4, 2011 : 8:30 AM to 10:20 AM
Sponsor: Biopharmaceutical Section
Abstract - #303060
Title: A Comparison of Methods for the Analysis of Continuous Data from Sequential Parallel Comparison Design Trials
Author(s): Allison Meisner*+ and Michael Pencina
Companies: Boston University and Boston University
Address: 86 Elm St, Somerville, MA, 02144, US
Keywords: clinical trials ; mixed models ; SPCD trials
Abstract:

Sequential parallel comparison design (SPCD) trials have been proposed by Fava et al as a way to determine the efficacy of a drug in settings where a high placebo response rate is common, specifically in the field of psychiatry. Such trials involve two phases with one or more assessments in each phase. In the first phase all participants are randomized to either treatment or placebo, while in the second phase placebo non-responders from phase I are re-randomized. The data are pooled across phases to assess treatment efficacy. For the analysis of continuous data, seemingly unrelated and ordinary least squares regressions have been suggested. We propose using linear mixed models for this data as such models are well-suited to the analysis of correlated data and allow efficient incorporation of missing data patterns. We use Monte Carlo methods to simulate data sets with different effect size and correlation structures between the two phases and apply the three methods outlined above to assess their power and protection of type I error. Their application is illustrated using data from an actual SPCD trial.


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