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Propensity score (PS) weighting methods are commonly used to adjust for confounding in observational treatment comparisons. However, in the setting of substantial covariate imbalance, PS values may approach 0 and 1, yielding extreme weights and inflated variance of the estimated treatment effect. Adaptations of the standard inverse probability of treatment weights (IPTW) can reduce the influence of extremes, including trimming methods that exclude people with PS values near 0 or 1. Alternatively, overlap weighting (OW) optimizes criteria related to bias and variance, and performs well compared to other PS weighting and matching methods. However, it has not been compared to propensity score stratification (PSS). PSS has some of the same potential advantages; being insensitive extreme values. We sought to compare these methods in the setting of substantial covariate imbalance to generate practical recommendations. Analytical derivations were used to establish connections between methods, and simulation studies were conducted to assess bias and variance of alternative methods. We find that OW is generally superior, particularly as covariate imbalance increases. In addition, a common method for implementing PSS based on Mantel–Haenszel weights (PSS-MH) is equivalent to a coarsened version of OW and can perform nearly as well. Finally, trimming methods increase bias across methods (IPTW, PSS and PSS-MH) unless the PS model is re-fit to the trimmed sample and weights or strata are re-derived. After trimming with re-fitting, all methods perform similarly to OW. These results may guide the selection, implementation and reporting of PS methods for observational studies with substantial covariate imbalance. [ABSTRACT FROM AUTHOR]

The inverse probability of treatment weighting (IPTW) approach is popular for evaluating causal effects in observational studies, but extreme propensity scores could bias the estimator and induce excessive variance. Recently, the overlap weighting approach has been proposed to alleviate this problem, which smoothly down-weights the subjects with extreme propensity scores. Although advantages of overlap weighting have been extensively demonstrated in literature with continuous and binary outcomes, research on its performance with time-to-event or survival outcomes is limited. In this article, we propose estimators that combine propensity score weighting and inverse probability of censoring weighting to estimate the counterfactual survival functions. These estimators are applicable to the general class of balancing weights, which includes IPTW, trimming, and overlap weighting as special cases. We conduct simulations to examine the empirical performance of these estimators with different propensity score weighting schemes in terms of bias, variance, and 95% confidence interval coverage, under various degrees of covariate overlap between treatment groups and censoring rates. We demonstrate that overlap weighting consistently outperforms IPTW and associated trimming methods in bias, variance, and coverage for time-to-event outcomes, and the advantages increase as the degree of covariate overlap between the treatment groups decreases. [ABSTRACT FROM AUTHOR]

Observational studies of treatment effects attempt to mimic a randomized experiment by balancing the covariate distribution in treated and control groups, thus removing biases related to measured confounders. Methods such as weighting, matching, and stratification, with or without a propensity score, are common in cross-sectional data. When treatments are initiated over longitudinal follow-up, a target pragmatic trial can be emulated using appropriate matching methods. The ideal experiment of interest is simple; patients would be enrolled sequentially, randomized to one or more treatments and followed subsequently. This tutorial defines a class of longitudinal matching methods that emulate this experiment and provides a review of existing variations, with guidance regarding study design, execution, and analysis. These principles are illustrated in application to the study of statins on cardiovascular outcomes in the Framingham Offspring cohort. We identify avenues for future research and highlight the relevance of this methodology to high-quality comparative effectiveness studies in the era of big data. [ABSTRACT FROM AUTHOR]

The popular inverse probability weighting method in causal inference is often hampered by extreme propensity scores, resulting in biased estimates and excessive variance. A common remedy is to trim patients with extreme scores (i.e. remove them from the weighted analysis). However, such methods are often sensitive to the choice of cutoff points and discard a large proportion of the sample. The implications for bias and the precision of the treatment effect estimate are unclear. These problems are mitigated by a newly developed method, the overlap weighting method. Overlap weights emphasize the target population with the most overlap in observed characteristics between treatments, by continuously down-weighting the units in the tails of the propensity score distribution. Here we use simulations to compare overlap weights to standard inverse probability weighting with trimming, in terms of bias, variance, and 95% confidence interval coverage. A range of propensity score distributions are considered, including settings with substantial nonoverlap and extreme values. To facilitate practical implementation, we further provide a consistent estimator for the standard error of the treatment effect estimated using overlap weighting. [ABSTRACT FROM AUTHOR]

Background: In 2009, national legislation promoted wide-spread adoption of electronic health records (EHRs) across US hospitals; however, the association of EHR use with quality of care and outcomes after acute myocardial infarction (AMI) remains unclear.Methods and Results: Data on EHR use were collected from the American Hospital Association Annual Surveys (2007-2010) and data on AMI care and outcomes from the National Cardiovascular Data Registry Acute Coronary Treatment and Interventions Outcomes Network Registry-Get With The Guidelines. Comparisons were made between patients treated at hospitals with fully implemented EHR (n=43 527), partially implemented EHR (n=72 029), and no EHR (n=9270). Overall EHR use increased from 82.1% (183/223) hospitals in 2007 to 99.3% (275/277) hospitals in 2010. Patients treated at hospitals with fully implemented EHRs had fewer heparin overdosing errors (45.7% versus 72.8%; P<0.01) and a higher likelihood of guideline-recommended care (adjusted odds ratio, 1.40 [confidence interval, 1.07-1.84]) compared with patients treated at hospitals with no EHR. In non-ST-segment-elevation AMI, fully implemented EHR use was associated with lower risk of major bleeding (adjusted odds ratio, 0.78 [confidence interval, 0.67-0.91]) and mortality (adjusted odds ratio, 0.82 [confidence interval, 0.69-0.97]) compared with no EHR. In ST-segment-elevation MI, outcomes did not significantly differ by EHR status.Conclusions: EHR use has risen to high levels among hospitals in the National Cardiovascular Data Registry. EHR use was associated with less frequent heparin overdosing and modestly greater adherence to acute MI guideline-recommended therapies. In non-ST-segment-elevation MI, slightly lower adjusted risk of major bleeding and mortality were seen in hospitals implemented with full EHRs; however, in ST-segment-elevation MI, differences in outcomes were not seen. [ABSTRACT FROM AUTHOR]

A correction to the article "Addressing Extreme Propensity Schores via the Overlap Weights" in a 2020 issue is presented.