Optimization-Based Stable Balancing Weights Versus Propensity Score Weighting for Samples With High Covariate Imbalance.

PURPOSE: To compare the performance (covariate balance, effective sample size [ESS]) of stable balancing weights (SBW) versus propensity score weighting (PSW). Two applied cases were used to compare performance: (Case 1) extreme imbalance in baseline covariates between groups and (Case 2) substantial discrepancy in sample size between groups. METHODS: Using the Premier Healthcare Database, we selected patients who (Case 1) underwent a surgical procedure with one of two different bipolar forceps between January 2000 and June 2020, or (Case 2) a neurological procedure using one of two different nonabsorbable surgical sutures between January 2000 and March 2020. Average treatment effects on the treated (ATT) weights were generated based on selected covariates. SBW was implemented using two techniques: (1) "grid search" to find weights of minimum variance at the lowest target absolute standardized mean difference (SMD); (2) finding weights of minimum variance at prespecified SMD tolerance. PSW and SBW methods were compared on postweighting SMDs, the number of imbalanced covariates, and ESS of the ATT-weighted control group. RESULTS: In both studies, improved covariate balance was achieved with both SBW techniques. All methods suffered from postweighting ESS that was lower than the unweighted control group's original sample size; however, SBW methods achieved higher ESS for the control groups. Sensitivity analyses using SBW to apply variable-specific SMD thresholds increased ESS, outperforming PSW. CONCLUSIONS: In this applied example, the optimization-based SBW method provided ample flexibility with respect to prespecification of covariate balance goals and resulted in better postweighting covariate balance and larger ESS as compared with PSW.

Risk of anastomotic leakage with two-row versus three-row manual circular staplers in colorectal anastomosis: a U.S. cohort study.

PURPOSES: To compare the risk of anastomotic leak (AL) between Ethicon manual circular staplers (two-row) versus Medtronic EEA™ circular stapler with Tri-Staple™ technology (three-row) and between Medtronic EEA™ circular stapler with DST™ Series technology (two-row) versus Tri-Staple™ technology. METHODS: A retrospective cohort study was conducted in adult patients who underwent a left-sided colorectal surgery 2019-2022 in U.S. Premier Healthcare Database to assess the risk of AL within 30 days post-index procedure. The study devices were Ethicon manual circular staplers, Medtronic EEA™ circular stapler with DST™ technology, and Medtronic EEA™ circular stapler with Tri-Staple™ technology. RESULTS: Across 447 hospitals, the cumulative incidences (95% confidence intervals [CI]) of AL within 30 days post-index procedure were 7.78% (6.91-8.74%) among 8337 patients in the Ethicon manual circular stapler cohort, 7.54% (6.87-8.27%) among 7928 patients in the Medtronic EEA™ circular stapler with DST™ technology cohort, and 8.19% (6.57-10.07%) among 1306 patients in the Medtronic EEA™ circular stapler with Tri-Staple™ technology cohort. Comparative analyses revealed no difference comparing Ethicon manual circular staplers with Medtronic EEA™ circular staplers with Tri-Staple™ technology (risk ratio [RR], 0.72; 95% CI, 0.52-1.01) or comparing Medtronic EEA™ circular staplers with DST™ technology to Tri-Staple™ technology (RR, 0.75; 95% CI, 0.53-1.06). CONCLUSION: In this analysis of a large cohort of patients undergoing a left-sided colorectal surgery from a U.S. hospital database, the risk of AL observed with manual two-row circular staplers was similar to that seen with three-row devices. This study affirms the safety of manual two-row circular staplers in colorectal anastomosis.

Causal mediation and sensitivity analysis for mixed-scale data.

The goal of causal mediation analysis, often described within the potential outcomes framework, is to decompose the effect of an exposure on an outcome of interest along different causal pathways. Using the assumption of sequential ignorability to attain non-parametric identification, Imai et al. (2010) proposed a flexible approach to measuring mediation effects, focusing on parametric and semiparametric normal/Bernoulli models for the outcome and mediator. Less attention has been paid to the case where the outcome and/or mediator model are mixed-scale, ordinal, or otherwise fall outside the normal/Bernoulli setting. We develop a simple, but flexible, parametric modeling framework to accommodate the common situation where the responses are mixed continuous and binary, and, apply it to a zero-one inflated beta model for the outcome and mediator. Applying our proposed methods to the publicly-available JOBS II dataset, we (i) argue for the need for non-normal models, (ii) show how to estimate both average and quantile mediation effects for boundary-censored data, and (iii) show how to conduct a meaningful sensitivity analysis by introducing unidentified, scientifically meaningful, sensitivity parameters.