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A Generalized Partially Linear Model for the Analysis of Quality of Life Data

2017-09-26
 

Academic Report

Title: A Generalized Partially Linear Model for the Analysis of Quality of Life Data

Reporter: TU Dongsheng (Queen’s University)

Time: September 26, 2017 (Tuesday) AM 10:00-11:30

Location: A#1101 room, Innovation Park Building

Contact: SONG Hui (tel: 84708351-8123)

 

Abstract: In this talk we present a generalized partially linear mean-covariance regression model for the analysis of Quality of Life (QoL) data, which are routinely collected in cancer clinical trials and bounded in a closed interval. Cholesky decomposition of the covariance matrix for within-subject responses and generalized estimation equations are used to estimate unknown parameters and the nonlinear function in the model. Simulation studies are performed to evaluate the performance of the proposed estimation procedures. Our new model is also applied to analyze the data from a cancer clinical trial that motivated this research. In comparison with available models in the literature, the proposed model does not require specific parametric assumptions on the density function of the longitudinal responses and the probability function of the boundary values and can capture dynamic changes of time or other interested variables on both mean and covariance of the correlated responses.

 

The brief introduction to the reporter: Dr. Dongsheng Tu is a professor of biostatistics in the Departments of Public Health Science and Mathematics & Statistics of Queen’s University at Kingston, Canada. He is also the Group Statistician at Canadian Cancer Trials Group responsible for the design and analysis of clinical trials conducted by the group. He is interested in the development of statistical theory and methods for the analysis of data from cancer clinical trials and has published one book and over 200 book chapters and papers in both statistical and medical journals such as Biometrika, Statistics in Medicine, and New England Journal of Medicine which attracted more than 30,000 citations.