A threshold linear mixed model for identification of treatment-sensitive subsets in a clinical trial based on
Xinyi Ge1, Yingwei Peng1,2, Dongsheng Tu1,2,3
1Department of Mathematics and Statistics, Queen's University, Kingston, Canada.
Abstract:
Identification of a subset of patients who may be sensitive to a specific treatment is an important problem in clinical trials. In this paper, we consider the case where the treatment effect is measured by longitudinal outcomes, such as quality of life scores assessed over the duration of a clinical trial, and the subset is determined by a continuous baseline covariate, such as age and expression level of a biomarker. A threshold linear mixed model is introduced, and a smoothing maximum likelihood method is proposed to obtain the estimation of the parameters in the model. Broyden-Fletcher-Goldfarb-Shanno algorithm is employed to maximize the proposed smoothing likelihood function. The proposed procedure is evaluated through simulation studies and application to the analysis of data from a randomized clinical trial on patients with advanced colorectal cancer.
More Related Videos
06:55Inverse Probability of Treatment Weighting Propensity Score using the Military Health System Data Repository and National Death Index
Published on: January 8, 2020
04:35Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach
Published on: July 3, 2020
Related Concept Videos
Longitudinal Studies
Comparing the Survival Analysis of Two or More Groups
Survival Tree
Building a Survival Tree
Constructing a...
Longitudinal Research
Bioequivalence Experimental Study Designs: Repeated Measures, Cross-Over, Carry-Over, and Latin Square Designs
Mechanistic Models: Compartment Models in Individual and Population Analysis
