Propensity score matching with R: conventional methods and new features

Abstract

It is increasingly important to accurately and comprehensively estimate the effects of particular clinical treatments. Although randomization is the current gold standard, randomized controlled trials (RCTs) are often limited in practice due to ethical and cost issues. Observational studies have also attracted a great deal of attention as, quite often, large historical datasets are available for these kinds of studies. 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.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. Within this approach, propensity score matching (PSM) has been empirically proven, with outstanding performances across observational datasets. While PSM tutorials are available in the literature, there is still room for improvement. Some PSM tutorials provide step-by-step guidance, but only one or two packages have been covered, thereby limiting their scope and practicality. 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. 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. This tutorial offers researchers with a broad survey of PSM, ranging from data preprocessing to estimations of propensity scores, and from matching to analyses. We also explain generalized propensity scoring for multiple or continuous treatments, as well as time-dependent PSM. Lastly, we discuss the advantages and disadvantages of propensity score methods.

Pre-operative knee extensor and flexor torque after secondary ACL rupture: a comparative retrospective analysis

“…After screening, n = 23 patients with isolated secondary ACL rupture remained. These individuals were then matched with an equal number of patients with primary ACL rupture and healthy controls by (1) weight, (2) height, (3) BMI, (4) age and (5) sex, using nearest neighbor propensity score matching [ 38 ]. Fitness and activity levels were comparable between groups as all participants underwent identical rehabilitation protocols 9 months after the surgical treatment [ 26 ] (Fig.…”

Diversity and seasonal abundance of Culicoides (Diptera, Ceratopogonidae) in Shizong County, Yunnan Province, China

“…Total specimens of each species were counted and where needed, collated into monthly averages. A heatmap of weekly totals was constructed by the R programming language [ 12 , 20 , 41 ] using the pheatmap package [ 16 ]. Rainfall and temperature data for Shizong County of Yunnan Province between May 2020 and May 2021 were gleaned from the Chinese historic weather website [ 1 ].…”

The long-term effect of job mobility on workers’ mental health: a propensity score analysis

“…A propensity score analysis was done to balance workers with experience of external mobility (treated group) and workers without this experience (untreated group) to adjust for systematic differences occurring in covariates that are linked to the outcome [ 25 , 26 ]. The propensity score approach is a quasi-experimental technique widely used in the field of observational studies to mimic randomization of clinical trials [ 27 ].…”