Can propensity score matching replace randomized controlled trials?

Table 1 Comparison of randomization methods for clinical trials

Method	Description	Ref.
Simple randomization	Each participant has an equal chance of being assigned to any of the treatment groups. This method is easy to implement and unpredictable, but it may result in unequal group sizes or imbalances in important covariates, especially in small studies	Grimm and Müller[75], 1999
Block randomization	Participants are allocated to treatment groups in blocks of fixed size, such as 4 or 6. This method ensures that the group sizes are balanced at any point of the study, but it may introduce some predictability if the block size is known or guessed by the investigators	Sreedevi et al[76], 2017
Stratified randomization	Participants are first stratified by one or more relevant factors, such as age, gender, or disease severity, and then randomized within each stratum. This method ensures that the treatment groups are balanced with respect to the stratification factors, but it may increase the complexity and cost of the randomization process	Kahan and Morris[21], 2012
Minimization	Participants are allocated to the treatment group that minimizes the imbalance in a set of predefined factors, such as prognostic variables or previous treatments. This method is adaptive and can achieve better balance than stratified randomization, but it may also introduce some predictability and bias if the allocation is not concealed	Treasure and MacRae[77], 1998

Table 2 Comparison of methods of computing propensity scores

Method	Advantages	Disadvantages	Ref.
Logistic regression	Simple and widely used	May not capture complex or nonlinear relationships	Otok et al[6], 2017
	Can handle binary and continuous covariates	May be sensitive to model misspecification
	Can estimate the propensity score and the treatment effect in one model	May not balance all covariates well
Discriminant analysis	Can handle multiclass treatment	May not capture nonlinear relationships	Rudner and Johnette[7], 2006
	Can capture linear combinations of covariates	May be sensitive to outliers and distributional assumptions
	Can handle multicollinearity among covariates	May not balance all covariates well
Random forests	Can handle complex and nonlinear relationships	May be computationally intensive	Zhao et al[8], 2016
	Can handle binary, categorical, and continuous covariates	May overfit the data
	Can balance all covariates well	May not estimate the propensity score and the treatment effect in one model

Table 3 Possible matching methods utilized in propensity score matching studies

Matching method	Indication
One-to-one	This method matches each treated unit with one control unit that has the closest propensity score. This method is simple and intuitive, but it may discard some units that are not matched
One-to-many	This method matches each treated unit with more than one control unit that has similar propensity scores. This method can increase the sample size and precision, but it may also introduce more bias due to imperfect matches
Nearest neighbor	This method matches each treated unit with the control unit that has the nearest propensity score, within a specified caliper or threshold. This method can reduce bias by excluding poor matches, but it may also reduce efficiency by excluding good matches
Caliper	This method matches each treated unit with the control unit that has the propensity score within a specified range or distance. This method can ensure a high degree of similarity between the matched pairs, but it may also result in a loss of observations if the caliper is too narrow
Stratification	This method divides the propensity score distribution into a number of strata or intervals, and then compares the outcomes of the treated and control units within each stratum. This method can balance the covariates across the strata, but it may also produce heterogeneous treatment effects across the strata

Table 4 Summary of the advantages of propensity score matching and randomized controlled trials

Propensity score matching	RCTs
Allows for utilization of retrospective data where randomization was not done	Gold standard for causal inference by eliminating bias
Improves efficiency of subject enrolment in prospective studies	Required as part of regulatory requirements
Allows analysis of causal inference in investigations where ethical considerations forbid RCTs	Allows researchers to conduct targeted studies to answer specific questions
Better external validity and generalizability	Better internal validity
Avoidance of type II errors
Shorter timeline to study completion

RCTs: Randomized controlled trials.