Comparisons of two statistical approaches in studying the longitudinal data: the multilevel model and the latent growth curve model.
- Author:
Lixia LI
1
;
Shudong ZHOU
1
;
Min ZHANG
1
;
Yanbo ZHANG
2
;
Yanhui GAO
3
Author Information
1. Department of Epidemiology and Biostatistics, School of Public Health, Guangdong Pharmaceutical University, Guangdong Key Laboratory of Molecular Epidemiology, Guangzhou 510310, China.
2. Department of Epidemiology and Biostatistics, School of Public Health, Shanxi Medical University.
3. Department of Epidemiology and Biostatistics, School of Public Health, Guangdong Pharmaceutical University, Guangdong Key Laboratory of Molecular Epidemiology, Guangzhou 510310, China. Email: gao_yanhui@163.com.
- Publication Type:Journal Article
- MeSH:
Colorectal Neoplasms;
surgery;
Humans;
Longitudinal Studies;
Models, Statistical;
Multilevel Analysis;
Postoperative Period;
Quality of Life
- From:
Chinese Journal of Epidemiology
2014;35(6):741-744
- CountryChina
- Language:Chinese
-
Abstract:
To compare two commonly used statistical approaches:the multilevel model and the latent growth curve model in analyzing longitudinal data. A longitudinal data set, obtained from the quality of life in patients with colorectal cancer after operation, was used to illustrate the similarities and differences between the two methods. Results from the study indicated that the latent growth curve modeling was equivalent to multilevel modeling with regards to longitudinal data which could yield identical results for the estimates of parameters. Multilevel model approach seemed easier for model specification. However, latent growth curve model had the advantage of providing model evaluation and was more flexible in statistical modeling by allowing the incorporation of latent variables. Both multilevel and latent growth curve models were suitable for analyzing longitudinal data with advantages on their own, they could be chosen by researchers under different situation to be chosen accordingly by researchers under different situation.
