Volume 21, Issue 3 (7-2022)
JRUMS 2022, 21(3): 343-366 |
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Tahmasebi fard M, Najafi F, Rezaeian S, Moradinazar M. How Can Causal Relationships Be Measured in Observational Studies? Propensity Score Matching: A Tutorial Article. JRUMS 2022; 21 (3) :343-366
URL: http://journal.rums.ac.ir/article-1-6506-en.html
URL: http://journal.rums.ac.ir/article-1-6506-en.html
مرکز تحقیقات بیماری های رفتاری
Abstract: (765 Views)
Background and Objectives: In most observational studies, researchers try to find causal relationships between observations. This correlation is associated with error due to lack of controlling confounding variables. One of the methods used to control the confounding variables is propensity score matching. Therefore, the purpose of this study is to explain the steps of propensity score matching.
Materials and Methods: Propensity score matching has 5 steps. The first step is to estimate the propensity score, which includes selecting the appropriate model and variables. The second step is to select an appropriate matching method based on the estimated propensity scores. The third step is overlap and common support. At this stage, observations that are outside the range of matching scores are removed. The quality of the match is then evaluated, and finally the sensitivity of the estimated effects must be estimated.
Results: In the propensity score matching, the observed basic variables are balanced between the exposure and non-exposure groups. However, if the statistical model used to calculate the propensity score is not correctly selected, an imbalance between the basic characteristics of the two groups can still exist.
Conclusion: Propensity score matching is useful in cases where the confounding variables of study are high. In observational studies, this method can be used as an alternative to randomization in experimental studies, which makes the estimates more accurate by reducing the selection bias and controlling confounding variables.
Key words: Propensity score matching, Confounding variables, Observational studies
Funding: This study did not have any funds.
Conflicts of interest: None declared.
Ethical approval: The Ethics Committee of Kermanshah University of Medical Sciences approved the study (IR.KERMANSHAH.KUMS.REC.1399.1168).
How to cite this article: Tahmasebi Fard Mina, Najafi Farid, Rezaeian Shahab, Moradinazar Mehdi. How Can Causal Relationships Be Measured in Observational Studies? Propensity Score Matching: A Tutorial Article. J Rafsanjan Univ Med Sci 2022; 21 (03): 343-66. [Farsi]
Materials and Methods: Propensity score matching has 5 steps. The first step is to estimate the propensity score, which includes selecting the appropriate model and variables. The second step is to select an appropriate matching method based on the estimated propensity scores. The third step is overlap and common support. At this stage, observations that are outside the range of matching scores are removed. The quality of the match is then evaluated, and finally the sensitivity of the estimated effects must be estimated.
Results: In the propensity score matching, the observed basic variables are balanced between the exposure and non-exposure groups. However, if the statistical model used to calculate the propensity score is not correctly selected, an imbalance between the basic characteristics of the two groups can still exist.
Conclusion: Propensity score matching is useful in cases where the confounding variables of study are high. In observational studies, this method can be used as an alternative to randomization in experimental studies, which makes the estimates more accurate by reducing the selection bias and controlling confounding variables.
Key words: Propensity score matching, Confounding variables, Observational studies
Funding: This study did not have any funds.
Conflicts of interest: None declared.
Ethical approval: The Ethics Committee of Kermanshah University of Medical Sciences approved the study (IR.KERMANSHAH.KUMS.REC.1399.1168).
How to cite this article: Tahmasebi Fard Mina, Najafi Farid, Rezaeian Shahab, Moradinazar Mehdi. How Can Causal Relationships Be Measured in Observational Studies? Propensity Score Matching: A Tutorial Article. J Rafsanjan Univ Med Sci 2022; 21 (03): 343-66. [Farsi]
Type of Study: Applicable |
Subject:
Statistics& Epidemiology
Received: 2022/04/19 | Accepted: 2022/06/29 | Published: 2022/07/19
Received: 2022/04/19 | Accepted: 2022/06/29 | Published: 2022/07/19
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