Application of longitudinal matching in causal inference for rolling enrollment interventions_Chinese Journal of Evidence-Based Medicine

Authors：

YANG Rongzhen ,  GUO Siwen

Department of Psychology, Renmin University of China, Beijing 100872, P. R. China;

Corresponding author：

GUO Siwen, Email: guosiwen@ruc.edu.cn

Keywords：

Matching; Comparison groups; Casual inference; Treatment effect

DOI：

10.7507/1672-2531.202310083

Video：

Export PDF Favorites Scan Get Citation

Abstract Full text Figures/Tables Video References Cited by

Rolling enrollment is a common method for participant recruitment in medical practice. In the longitudinal data, where researchers are often interested in outcomes occurring after a certain period of treatment, the definition of causal effects differs from that in the cross-sectional data. It poses new challenges for the application of matching methods in the longitudinal studies. Longitudinal matching is an extension of matching methods in longitudinal studies involving static interventions such as rolling enrollment. Currently, longitudinal matching methods are widely applied in the comparative effectiveness research. This article elucidates the fundamental principles, applicable conditions, code implementation, and application instances of four longitudinal matching methods through theoretical discussions and empirical illustrations. It provides methodological references for estimating causal effects in longitudinal data analysis.

Citation： YANG Rongzhen, GUO Siwen. Application of longitudinal matching in causal inference for rolling enrollment interventions. Chinese Journal of Evidence-Based Medicine, 2024, 24(6): 730-738. doi: 10.7507/1672-2531.202310083 Copy

1.
2.
3.
4.
5.
6.
7.
8.	Hernán MA, Robins JM. Causal inference: what if. Boca Raton: Chapman & Hall/CRC. , 2020.
9.
10.	郑全庆. 临床流行病学. 西安: 西安交通大学出版社, 2007.
11.
12.	郭申阳, Fraser MW. 倾向值分析: 统计方法与应用. 重庆: 重庆大学出版社, 2012.
13.
14.
15.
16.	邱嘉平. 因果推断实用计量方法. 上海: 上海财经大学出版社, 2020.
17.
18.
19.
20.
21.
22.
23.
24.
25.
26.
27.
28.
29.
30.	Rosenbaum PR. Observational studies. New York: Springer, 2002.
31.	Sun Y. New matching algorithm: outlier first matching (OFM) and its performance on propensity score analysis (PSA) under new stepwise matching framework (SMF). New York: State University of New York at Albany, 2014.
32.
33.
34.
35.	Keane MH. The impact of online art therapy groups for pediatric rheumatology patients. Rheumatology, 2022, 61(Sup 1): c133-c168.
36.
37.
38.
39.
40.

1.
2.
3.
4.
5.
6.
7.
8. Hernán MA, Robins JM. Causal inference: what if. Boca Raton: Chapman & Hall/CRC. , 2020.
9.
10. 郑全庆. 临床流行病学. 西安: 西安交通大学出版社, 2007.
11.
12. 郭申阳, Fraser MW. 倾向值分析: 统计方法与应用. 重庆: 重庆大学出版社, 2012.
13.
14.
15.
16. 邱嘉平. 因果推断实用计量方法. 上海: 上海财经大学出版社, 2020.
17.
18.
19.
20.
21.
22.
23.
24.
25.
26.
27.
28.
29.
30. Rosenbaum PR. Observational studies. New York: Springer, 2002.
31. Sun Y. New matching algorithm: outlier first matching (OFM) and its performance on propensity score analysis (PSA) under new stepwise matching framework (SMF). New York: State University of New York at Albany, 2014.
32.
33.
34.
35. Keane MH. The impact of online art therapy groups for pediatric rheumatology patients. Rheumatology, 2022, 61(Sup 1): c133-c168.
36.
37.
38.
39.
40.

Previous Article
Application of bnma package of R software in Bayesian network meta-analysis
Next Article
Overview of the assessment methods for muscle spasm after stroke

Chinese Journal of Evidence-Based Medicine

Application of longitudinal matching in causal inference for rolling enrollment interventions

Abstract Full text Figures/Tables Video References Cited by

Previous Article

Next Article

Format

Content