To extend previous simulations on the performance of propensity score (PS) weighting and trimming methods to settings without and with unmeasured confounding, Poisson outcomes, and various strengths of treatment prediction (PS c-statistic), we simulated studies with a binary intended treatment T as a function of 4 measured covariates. We mimicked treatment withheld and last-resort treatment by adding two "unmeasured" dichotomous factors that directed treatment to change for some patients in both tails of the PS distribution. The number of outcomes Y was simulated as a Poisson function of T and confounders. We estimated the PS based on measured covariates and trimmed the tails of the PS distribution using three strategies ("Crump", "Stürmer", and "Walker"). After trimming and re-estimation, we used alternative PS weights to estimate the treatment effect (rate ratio): IPTW, SMR-treated, SMR-untreated, overlap (ATO), matching, and entropy. With no unmeasured confounding, ATO (123%) and "Crump" trimming (112%) improved relative efficiency compared with untrimmed IPTW. With unmeasured confounding, untrimmed estimates were biased irrespective of weighting method and only Stürmer and Walker trimming consistently reduced bias. In settings where unmeasured confounding (e.g., frailty) may lead physicians to withhold treatment, Stürmer and Walker trimming should be considered before primary analysis.
