A threshold longitudinal Tobit quantile regression model for identification of treatment-sensitive subgroups based on interval-bounded longitudinal measurements and a continuous covariate.
Tobit model
longitudinal outcomes
quantile regression
random weighting
subgroup analysis
threshold regression
Journal
Statistics in medicine
ISSN: 1097-0258
Titre abrégé: Stat Med
Pays: England
ID NLM: 8215016
Informations de publication
Date de publication:
10 Nov 2023
10 Nov 2023
Historique:
revised:
12
06
2023
received:
14
12
2022
accepted:
01
08
2023
pubmed:
21
8
2023
medline:
21
8
2023
entrez:
20
8
2023
Statut:
ppublish
Résumé
Identification of a subgroup of patients who may be sensitive to a specific treatment is an important problem in precision medicine. This article considers the case where the treatment effect is assessed by longitudinal measurements, 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. Recently, a linear mixed threshold regression model has been proposed but it assumes the longitudinal measurements are normally distributed. In many applications, longitudinal measurements, such as quality of life data obtained from answers to questions on a Likert scale, may be restricted in a fixed interval because of the floor and ceiling effects and, therefore, may be skewed. In this article, a threshold longitudinal Tobit quantile regression model is proposed and a computational approach based on alternating direction method of multipliers algorithm is developed for the estimation of parameters in the model. In addition, a random weighting method is employed to estimate the variances of the parameter estimators. The proposed procedures are evaluated through simulation studies and applications to the data from clinical trials.
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
4618-4631Subventions
Organisme : National Natural Science Foundation of China
ID : 11971457
Organisme : Natural Science Foundation of Anhui Province
ID : 1908085MA06
Organisme : Natural Science Foundation of Anhui Province
ID : 2208085QA06
Organisme : Natural Sciences and Engineering Research Council of Canada
Informations de copyright
© 2023 John Wiley & Sons Ltd.
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