Liau Matthias Yi Quan, Toh En Qi, Muhamed Shamir, Selvakumar Surya Varma, Shelat Vishalkumar Girishchandra
Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore 308232, Singapore.
Department of General Surgery, Tan Tock Seng Hospital, Singapore 308433, Singapore.
World J Methodol. 2024 Mar 20;14(1):90590. doi: 10.5662/wjm.v14.i1.90590.
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.
长期以来，随机对照试验（RCT）一直被视为临床研究中确立因果关系的金标准。尽管如此，RCT存在各种局限性，阻碍了其广泛应用，这些局限性包括对一组患者 withholding 可能挽救生命的治疗的伦理问题，以及由于严格的纳入标准导致的相对较差的外部效度等。然而，随着倾向得分匹配（PSM）作为一种回顾性统计工具的引入，临床研究中确立因果关系的新领域被开辟出来。PSM利用来自现有来源（如登记处或电子健康记录）的观察数据预测治疗效果，根据倾向得分创建接受或未接受干预的匹配参与者样本，倾向得分考虑了年龄、性别和合并症等特征。鉴于其回顾性性质以及对现有来源观察数据的使用，PSM规避了RCT面临的上述伦理问题。大多数RCT排除老年人、孕妇和幼儿；因此，针对这一人群的治疗疗效证据很少通过有力的临床研究得到证实。另一方面，通过将研究患者特征与感兴趣人群（包括老年人、孕妇和幼儿）的特征相匹配，PSM使结果能够推广到更广泛的人群，从而大大提高了外部效度。PSM并非要取代RCT，而是将PSM与RCT协同整合，两种方法相互补充，有望提供更好的研究结果。例如，在一项RCT中，研究甘露醇对急性脑出血强化降压试验参与者结局的影响，尽管有随机化方案，但治疗组和对照组之间合并症和当前用药的基线特征仍存在显著差异。因此，在分析中纳入了PSM，以创建治疗组和对照组在这些基线特征方面相匹配的样本，从而为甘露醇的影响提供更公平的比较。这篇文献综述报告了将PSM与RCT结合使用的应用、优势和注意事项，说明了其在完善随机化、提高外部效度以及考虑方案不依从性方面的效用。未来的研究应考虑在RCT中整合PSM的使用，以便更好地将结果推广到临床实践的目标人群，从而使更广泛的患者受益，同时保持RCT提供的随机化的稳健性。
