CHEST Guidelines-The-Pros-and-Cons-of-Propensity-Scores

The-Pros-and-Cons-of-Propensity-Scores_chest

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The commentary by David L. Streiner and Geoffrey R. Norman in CHEST (2012) addresses the challenges and considerations of using propensity scores (PS) in non-randomized studies, particularly when randomized controlled trials (RCTs) are not feasible. The primary advantage of RCTs is randomization, which helps minimize baseline differences between groups and ensures that any differences observed are due to chance rather than systematic group differences. However, RCTs are not always possible, especially in situations where randomization would be unethical or impractical.<br /><br />In non-randomized studies, confounding variables pose significant challenges, as they can create or obscure relationships between interventions and outcomes. Propensity scores are introduced as a method to address these challenges by equating groups at baseline. They work by running a logistic regression with group membership as the dependent variable and potential confounders as predictors, generating a single score for each participant that summarizes the effects of these variables.<br /><br />While PSs offer a potentially powerful solution, the authors caution that their application is not straightforward and carries significant limitations. Choosing appropriate covariates for PS matching is crucial, and missing essential variables can lead to biased results. Moreover, matching individuals based on PSs can result in a loss of participants and may cause selected individuals not to represent their respective groups accurately.<br /><br />Additionally, the diversity of PS matching methods (such as nearest neighbor, caliper, and kernel matching) complicates analysis, as different methods can yield varying results due to inconsistencies in subgroup selection. Disagreements also persist on how matched data should be analyzed, highlighting the complexity of employing PSs effectively.<br /><br />Streiner and Norman underline the importance of conducting sensitivity analyses to ensure robust conclusions, given that improper use of PS can skew study outcomes. They emphasize that while propensity score matching is a valuable tool, it requires careful implementation and critical assessment to avoid unwarranted causal inferences.

Keywords

propensity scores

non-randomized studies

randomized controlled trials

confounding variables

logistic regression

covariates

matching methods

sensitivity analyses

causal inferences

baseline differences