Can propensity score matching replace randomized controlled trials?

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.