American Statistical Association
In many clinical studies, the disease of interest is multi-faceted and multiple outcomes are needed to adequately characterize the disease or its severity. In such studies, it is often difficult to determine what constitutes improvement due to the multivariate nature of the response. Furthermore, when the disease of interest has an unknown etiology and/or is primarily a symptom-defined syndrome, there is potential for the study population to be heterogeneous with respect to their symptom profiles. Identification of population subgroups is of interest as it may enable clinicians to provide targeted treatments or develop accurate prognoses. We propose a multivariate growth curve latent class model that group subjects based on multiple outcomes measured repeatedly over time. These groups or latent classes are characterized by distinctive longitudinal profiles of a latent variable which is used to summarize the multivariate outcomes at each point in time. The mean growth curve for the latent variable in each class defines the features of the class. We develop this model for any combination of continuous, binary, ordinal or count outcomes within a Bayesian hierarchical framework. Simulation studies are used to validate the estimation procedures. We apply our models to data from a randomized clinical trial evaluating the efficacy of Bacillus Calmette-Guerin in treating symptoms of IC where we are able to identify a class of subjects who were not responsive to treatment, and a class of subjects where treatment was effective in reducing symptoms over time.
Mary Sammel is an Associate Professor of Biostatistics at the University of Pennsylvania. She received her Sc.D. from Harvard University in 1995. Dr. Sammel's statistical specialties are in the area of multivariate and longitudinal data methods, as well as analysis of mixtures of discrete and continuous data. Research content areas of interest include environmental and reproductive health. Dr. Sammel has extensive experience as a data analyst and has worked with a variety of biomedical investigators.
Dr. Sammel's current work is in the area of latent variable modelling where the focus is to reduce multivariate data to a smaller number of dimensions and estimate the impact of covariates. This is one way to address the issue of multiple testing for multivariate data. It also provides a means for producing a relative ranking of the subjects with respect to the latent variable. Current manuscripts in progress will evaluate the performance of this global test to standard methods. Work is also in progress to expand programs to deal with ordered categorical responses, which are commonly used to develop health scales.
|Date:||Tuesday, November 29, 2011|
|Time:||3:30 - 4:30 P.M.|
New York State Psychiatric Institute
1051 Riverside Drive
6th Floor Floor Boardroom (6601)
New York, New York