To this end, both randomized controlled trials (RCTs) and observational studies are commonly used tools. It is essential to accurately estimate the effects of particular treatments or interventions in order to provide a solid evidential foundation for clinical practice. Lastly, we discuss the advantages and disadvantages of propensity score methods. We also explain generalized propensity scoring for multiple or continuous treatments, as well as time-dependent PSM. This tutorial offers researchers with a broad survey of PSM, ranging from data preprocessing to estimations of propensity scores, and from matching to analyses. To this end, this tutorial was developed with a six-step PSM framework, in which we summarize the recent updates and provide step-by-step guidance to the R programming language.
Several articles and books have expounded upon propensity scores in detail, exploring statistical principles and theories however, the lack of explanations on function usage in programming language has made it difficult for researchers to understand and follow these materials. Some PSM tutorials provide step-by-step guidance, but only one or two packages have been covered, thereby limiting their scope and practicality. While PSM tutorials are available in the literature, there is still room for improvement. Within this approach, propensity score matching (PSM) has been empirically proven, with outstanding performances across observational datasets.
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Propensity score methods, which are a series of balancing methods in these studies, have become increasingly popular by virtue of the two major advantages of dimension reduction and design separation. However, observational studies also have their drawbacks, mainly including the systematic differences in baseline covariates, which relate to outcomes between treatment and control groups that can potentially bias results. Observational studies have also attracted a great deal of attention as, quite often, large historical datasets are available for these kinds of studies. Although randomization is the current gold standard, randomized controlled trials (RCTs) are often limited in practice due to ethical and cost issues. It is increasingly important to accurately and comprehensively estimate the effects of particular clinical treatments.
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