In this prospective multicenter, observational cohort study of patients presenting at the ED for AHF, early treatment with intravenous loop diuretics was associated with lower in-hospital mortality. (Registry focused on very early presentation and treatment in emergency department of acute heart failure syndrome; UMIN000014105).

SUMMARY Over the past decade, an increasing number of observational studies have examined the effectiveness or safety of rheumatoid arthritis treatments. However, unlike randomized controlled trials (RCTs), observational studies of drug effects face methodological challenges including confounding by indication. Two design principles - active comparator design and new user design can help mitigate such challenges in observational studies. To improve validity of study findings, observational studies should be designed in such a way that makes them more closely approximate RCTs. The active comparator design compares the drug of interest to another commonly used agent for the same indication, rather than a ‘non-user’ group. This principle helps select treatment groups similar in treatment indications (both measured and unmeasured characteristics). The new user design includes a cohort of patients from the time of treatment initiation, so that it can assess patients’ pretreatment characteristics and capture all events occurring anytime during follow-up.

BACKGROUND Propensity score matching is a commonly used tool. However, its use in settings with more than two treatment groups has been less frequent. We examined the performance of a recently developed propensity score weighting method in the three treatment group setting. METHODS The matching weight method is an extension of inverse probability of treatment weighting (IPTW) that reweights both exposed and unexposed groups to emulate a propensity score matched population. Matching weights can generalize to multiple treatment groups. The performance of matching weights in the three-group setting was compared via simulation to three-way 1:1:1 propensity score matching and IPTW. We also applied these methods to an empirical example that compared the safety of three analgesics. RESULTS Matching weights had similar bias, but better mean squared error (MSE) compared to three-way matching in all scenarios. The benefits were more pronounced in scenarios with a rare outcome, unequally sized treatment groups, or poor covariate overlap. IPTW’s performance was highly dependent on covariate overlap. In the empirical example, matching weights achieved the best balance for 24 out of 35 covariates. Hazard ratios were numerically similar to matching. However, the confidence intervals were narrower for matching weights. CONCLUSIONS Matching weights demonstrated improved performance over three-way matching in terms of MSE, particularly in simulation scenarios where finding matched subjects was difficult. Given its natural extension to settings with even more than three groups, we recommend matching weights for comparing outcomes across multiple treatment groups, particularly in settings with rare outcomes or unequal exposure distributions.