Testing Treatment Efficacy in Clinical Studies Consisting of Multiple Subpopulations
Yeh-Fong Chen, US Food and Drug Administration *Fanhui Kong, FDA Keywords: clinical trials, MMRM, joint model, latent mixture model, dropout, treatment efficacy In psychiatric trials, when study populations often consist of several homogeneous subpopulations or clusters, where each has its own trajectory of mean measures over time and these clusters are often associated with the reasons of dropout. Regular likelihood methods such as MMRM may not give unbiased estimates for the overall treatment efficacy, even though the MAR is satisfied. In addition, regular joint models for outcomes and dropouts may not give unbiased results as well if data are missing not at random. We address this problem by assuming that each cluster has its own joint model. This would lead to the general latent mixture models. In the presentation, we will illustrate the problem and demonstrate how to derive the maximum likelihood estimates and test overall treatment efficacy. Our simulation results and the applications based on real data examples will also be provided.
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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