Comparison of propensity score matching with mixed-effect model in cluster randomized trial

ZHAO Hang; YU Xiaojin

doi:10.16462/j.cnki.zhjbkz.2024.01.018

Volume 28 Issue 1

Jan. 2024

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ZHAO Hang, YU Xiaojin. Comparison of propensity score matching with mixed-effect model in cluster randomized trial[J]. CHINESE JOURNAL OF DISEASE CONTROL & PREVENTION, 2024, 28(1): 112-116. doi: 10.16462/j.cnki.zhjbkz.2024.01.018

Citation:

ZHAO Hang, YU Xiaojin. Comparison of propensity score matching with mixed-effect model in cluster randomized trial[J]. CHINESE JOURNAL OF DISEASE CONTROL & PREVENTION, 2024, 28(1): 112-116. doi: 10.16462/j.cnki.zhjbkz.2024.01.018

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Comparison of propensity score matching with mixed-effect model in cluster randomized trial

doi: 10.16462/j.cnki.zhjbkz.2024.01.018

ZHAO Hang,
YU Xiaojin^,

Department of Epidemiology and Health Statistics, School of Public Health, Southeast University, Nanjing 210009, China

Funds:

National Natural Science Foundation of China 81673274

More Information

Corresponding author: YU Xiaojin, E-mail: xiaojinyu@seu.edu.cn
Received Date: 2022-07-28
Rev Recd Date: 2022-09-30

Available Online: 2024-02-05

Publish Date: 2024-01-10

Abstract

Abstract

Objective To compare the efficacy of propensity score matching (PSM) and mixed-effect model in statistical performance, providing methodological guidance for similar studies. Methods Employing a simulation study and a case study of acute ischemic stroke, this research compared the statistical performance of propensity score matching followed by univariate conditional logistic regression and mixed-effect model. It aims to illustrate their application scenarios and selection strategies in cluster randomized data involving confounders. Results In the simulation study, the balance of confounders between groups was significantly improved after PSM. Conditional logistic model provides similar treatment effect estimates but smaller P values compared with mixed-effect model. In the acute ischemic stroke case study, the standardized mean difference (SMD) of confounders were reduced below 0.1 after matching. Conditional logistic model identified the between-group difference in both outcomes. In contrast, mixed-effect model only identified the between-group difference in 7-day effective rate. Conclusions Propensity score matching can effectively improves groups comparability. Compared with mixed-effect model, conditional logistic model based on matched data has higher power of test. PSM is advisable for priority consideration in clinical cluster randomized trials, keeping in mind its specific application conditions and limitations.
- Cluster randomized trials,
- Propensity score,
- Mixed-effect model

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References(7)

References

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