A threshold linear mixed model for identification of treatment-sensitive subsets in a clinical trial based on longitudinal outcomes and a continuous covariate. (October 2020)

• Record Type: Journal Article
• Authors: Ge, Xinyi; Peng, Yingwei; Tu, Dongsheng
• Is Part Of: Statistical methods in medical research. Volume 29:Number 10(2020)
• Page Start: 2919
• Page End: 2931
• Publication Date: 2020-10
• Subjects: Subgroup analysis -- predictive biomarker -- smoothing likelihood function -- longitudinal data; Medicine -- Research -- Statistical methods -- Periodicals; Research -- Periodicals; Review Literature -- Periodicals; Statistics -- methods -- Periodicals; Médecine -- Recherche -- Méthodes statistiques -- Périodiques; 610.727
• DOI: 10.1177/0962280220912772
• Languages: English
• ISSNs: 0962-2802

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.