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World J Methodol. Mar 20, 2024; 14(1): 90590
Published online Mar 20, 2024. doi: 10.5662/wjm.v14.i1.90590
Can propensity score matching replace randomized controlled trials?
Matthias Yi Quan Liau, En Qi Toh, Shamir Muhamed, Surya Varma Selvakumar, Vishalkumar Girishchandra Shelat
Matthias Yi Quan Liau, En Qi Toh, Shamir Muhamed, Surya Varma Selvakumar, Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore 308232, Singapore
Vishalkumar Girishchandra Shelat, Department of General Surgery, Tan Tock Seng Hospital, Singapore 308433, Singapore
Vishalkumar Girishchandra Shelat, Surgical Science Training Centre, Tan Tock Seng Hospital, Singapore 308433, Singapore
Author contributions: Liau MYQ, Toh EQ, Muhamed S, Selvakumar SV and Shelat VG designed the research study; Liau MYQ, Toh EQ, Muhamed S, Selvakumar SV and Shelat VG performed the research; Liau MYQ, Toh EQ, Muhamed S, Selvakumar SV and Shelat VG analyzed the data and wrote the manuscript; All authors have read and approve the final manuscript.
Conflict-of-interest statement: All the authors report no relevant conflicts of interest for this article.
Open-Access: This article is an open-access article that was selected by an in-house editor and fully peer-reviewed by external reviewers. It is distributed in accordance with the Creative Commons Attribution NonCommercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See: https://creativecommons.org/Licenses/by-nc/4.0/
Corresponding author: Vishalkumar Girishchandra Shelat, FEBS, FRCS, MBBS, MMed, Adjunct Associate Professor, Department of General Surgery, Tan Tock Seng Hospital, 11 Jalan Tan Tock Seng, Singapore 308433, Singapore. vgshelat@gmail.com
Received: December 7, 2023
Peer-review started: December 7, 2023
First decision: December 23, 2023
Revised: January 5, 2024
Accepted: February 23, 2024
Article in press: February 23, 2024
Published online: March 20, 2024
Processing time: 90 Days and 17 Hours
Abstract

Randomized controlled trials (RCTs) have long been recognized as the gold standard for establishing causal relationships in clinical research. Despite that, various limitations of RCTs prevent its widespread implementation, ranging from the ethicality of withholding potentially-lifesaving treatment from a group to relatively poor external validity due to stringent inclusion criteria, amongst others. However, with the introduction of propensity score matching (PSM) as a retrospective statistical tool, new frontiers in establishing causation in clinical research were opened up. PSM predicts treatment effects using observational data from existing sources such as registries or electronic health records, to create a matched sample of participants who received or did not receive the intervention based on their propensity scores, which takes into account characteristics such as age, gender and comorbidities. Given its retrospective nature and its use of observational data from existing sources, PSM circumvents the aforementioned ethical issues faced by RCTs. Majority of RCTs exclude elderly, pregnant women and young children; thus, evidence of therapy efficacy is rarely proven by robust clinical research for this population. On the other hand, by matching study patient characteristics to that of the population of interest, including the elderly, pregnant women and young children, PSM allows for generalization of results to the wider population and hence greatly increases the external validity. Instead of replacing RCTs with PSM, the synergistic integration of PSM into RCTs stands to provide better research outcomes with both methods complementing each other. For example, in an RCT investigating the impact of mannitol on outcomes among participants of the Intensive Blood Pressure Reduction in Acute Cerebral Hemorrhage Trial, the baseline characteristics of comorbidities and current medications between treatment and control arms were significantly different despite the randomization protocol. Therefore, PSM was incorporated in its analysis to create samples from the treatment and control arms that were matched in terms of these baseline characteristics, thus providing a fairer comparison for the impact of mannitol. This literature review reports the applications, advantages, and considerations of using PSM with RCTs, illustrating its utility in refining randomization, improving external validity, and accounting for non-compliance to protocol. Future research should consider integrating the use of PSM in RCTs to better generalize outcomes to target populations for clinical practice and thereby benefit a wider range of patients, while maintaining the robustness of randomization offered by RCTs.

Keywords: Propensity score matching; Randomized controlled trials; Randomization; Clinical practice; Validity; Ethics

Core Tip: Several studies in the literature compare treatment effect estimates in propensity score matching studies and randomized controlled trials (RCTs), but few employ both methods synergistically in determining treatment outcomes. This is a first review to report and provide examples on how propensity score matching can be integrated into RCTs to refine randomization, account for non-compliance to protocol and improve external validity to produce more comprehensive and generalizable evidence for informed clinical decision making.