دورية أكاديمية
Regression analysis of longitudinal data with random change point.
| العنوان: | Regression analysis of longitudinal data with random change point. |
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| المؤلفون: | Zhang P; Center of Statistical Research, School of Statistics, Southwestern University of Finance and Economics, Chengdu, Sichuan, China., Chen X; Center of Statistical Research, School of Statistics, Southwestern University of Finance and Economics, Chengdu, Sichuan, China., Sun J; Department of Statistics, University of Missouri, Columbia, MO, USA. |
| المصدر: | Statistical methods in medical research [Stat Methods Med Res] 2024 Apr; Vol. 33 (4), pp. 634-646. Date of Electronic Publication: 2024 Feb 23. |
| نوع المنشور: | Journal Article |
| اللغة: | English |
| بيانات الدورية: | Publisher: SAGE Publications Country of Publication: England NLM ID: 9212457 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1477-0334 (Electronic) Linking ISSN: 09622802 NLM ISO Abbreviation: Stat Methods Med Res Subsets: MEDLINE |
| أسماء مطبوعة: | Publication: London : SAGE Publications Original Publication: Sevenoaks, Kent, UK : Edward Arnold, c1992- |
| مواضيع طبية MeSH: | Longitudinal Studies*, Humans ; Linear Models ; Regression Analysis ; Computer Simulation |
| مستخلص: | A great deal of literature has been established for regression analysis of longitudinal data and in particular, many methods have been proposed for the situation where there exist some change points. However, most of these methods only apply to continuous response and focus on the situations where the change point only occurs on the response or the trend of the individual trajectory. In this article, we propose a new joint modeling approach that allows not only the change point to vary for different subjects or be subject-specific but also the effect heterogeneity of the covariates before and after the change point. The method combines a generalized linear mixed effect model with a random change point for the longitudinal response and a log-linear regression model for the random change point. For inference, a maximum likelihood estimation procedure is developed and the asymptotic properties of the resulting estimators, which differ from the standard asymptotic results, are established. A simulation study is conducted and suggests that the proposed method works well for practical situations. An application to a set of real data on COVID-19 is provided. Competing Interests: Declaration of conflicting interestsThe author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article. |
| فهرسة مساهمة: | Keywords: Generalized linear mixed effect model; joint modeling; longitudinal data; random change point |
| تواريخ الأحداث: | Date Created: 20240224 Date Completed: 20240418 Latest Revision: 20240418 |
| رمز التحديث: | 20240418 |
| DOI: | 10.1177/09622802241232125 |
| PMID: | 38396379 |
| قاعدة البيانات: | MEDLINE |
Full Text Finder | تدمد: | 1477-0334 |
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| DOI: | 10.1177/09622802241232125 |