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Objective. To provide step-by-step guidance and STATA and R code for usingpropensity score (PS) weighting to estimate moderation effects. ResearchDesign. Tutorial illustrating the key steps for estimating and testingmoderation using observational data. Steps include (1) examining covariateoverlap across treatment groups within levels of the moderator, (2) estimatingthe PS weights, (3) evaluating whether PS weights improved covariate balance,(4) estimating moderated treatment effects, and (5) assessing sensitivity offindings to unobserved confounding. Our illustrative case study uses data from41,832 adults from the 2019 National Survey on Drug Use and Health to examineif gender moderates the association between sexual minority status (e.g.,lesbian, gay, or bisexual [LGB] identity) and adult smoking prevalence.Results. For our case study, there were no noted concerns about covariateoverlap and we were able to successfully estimate the PS weights within eachlevel of the moderator. Moreover, balance criteria indicated that PS weightssuccessfully achieved covariate balance for both moderator groups. PS weightedresults indicated there was significant evidence of moderation for the casestudy and sensitivity analyses demonstrated that results were highly robust forone level of the moderator but not the other. Conclusions. When conductingmoderation analyses, covariate imbalances across levels of the moderator cancause biased estimates. As demonstrated in this tutorial, PS weighting withineach level of the moderator can improve the estimated moderation effects byminimizing bias from imbalance within the moderator subgroups.

A tutorial for using propensity score weighting for moderation analysis: an application to smoking disparities among sexual minority adults