Laparoscopic vs.open surgery for gastrointestinal stromal tumors of esophagogastric junction:A multicenter,retrospective cohort analysis with propensity score weighting

Objective:Laparoscopic resection is increasingly performed for gastrointestinal stromal tumors(GISTs).However,the laparoscopic approach for GISTs located in the esophagogastric junction(EGJ-GIST)is surgically challenging.This study compares the efficacy of laparoscopic surgery and the open procedure for EGJ-GIST through the propensity score weighting(PSW)method.Methods:Between April 2006 and April 2018,1,824 surgical patients were diagnosed with primary gastric GIST at four medical centers in South China.Of these patients,228 were identified as EGJ-GISTs and retrospectively reviewed clinicopathological characteristics,operative information,and long-term outcomes.PSW was used to create the balanced cohorts.Results:PSW was carried out in laparoscopic and open-surgery cohorts according to year of surgery,sex,age,body mass index(BMI),tumor size,mitotic rates and recurrence risk.After PSW,438 patients consisting of 213 laparoscopic(L group)and 225 open surgery(O group)patients were enrolled.After PSW,the following measures in the L group were superior to those in the O group:median operative time[interquartile range(IQR)]:100.0(64.5-141.5)vs.149.0(104.0-197.5)min,P<0.001;median blood loss(IQR):30.0(10.0-50.0)vs.50.0(20.0-100.0)mL,P=0.002;median time to liquid intake(IQR):3.0(2.0-4.0)vs.4.0(3.0-5.0)d,P<0.001;median hospital stay(IQR):6.0(4.0-8.0)vs.7.0(5.0-12.0)d,P<0.001;and postoperative complications(10.3%vs.22.7%,P=0.001).The median follow-up was 55(range,2-153)months in the entire cohort.No significant differences were detected in either relapse-free survival(RFS)[hazard ratio(HR):0.372,95%confidence interval(95%CI):0.072-1.910,P=0.236]or overall survival(OS)(HR:0.400,95%CI:0.119-1.343,P=0.138)between the two groups.Conclusions:Laparoscopic surgery for EGJ-GIST is associated with the advantages of shorter operative time,reduced blood loss,shorter time to liquid intake,and shorter length of stay,all without compromising postoperative outcomes and long-term survival.

Temporal Variations in Mortality after Liver Transplantation: Retrospective Investigation of Potential Risk Factors Using Propensity Score

Objective: We aim to detect over-time variations in mortality of liver transplant recipients stratified by the period of transplant. Since this is a retrospective investigation, bias reduction caused by possible confounding effects can be achieved by using propensity score weighting in a multivariate logistic regression model. Methods: Medical charts of all adult liver transplant recipients (n = 250) who were transplanted in three periods 2005-2009, 2010-2014 and 2015-2019 were retrospectively reviewed. The following recipient factors were analyzed: recipients and donors’ ages, sex, renal impairment, body mass index (BMI), presence of bacterial or viral infections, MELD (Model for end-stage diseases). Multivariate logistic model adjusted by Propensity Scores (PS) was used to identify the effect of the risk factors on mortality, and death within five years, in the targeted time frame. Patient outcomes are recorded as;(patient status = 1 if dead, or patient status = 0 if alive). Results: Meld score, recipient age, and renal impairments were shown to be predictors of mortality in transplanted patients. Multivariate regression model was used to identify the significance of the specified risk factors, followed by pairwise comparisons between periods. Pairwise comparisons between periods using logistic regression weighted by the inverse propensity score, correcting for the possible confounding effect of measured covariates showed that the death rate is significantly reduced in subsequent periods as compared to the initial period. Conclusions: The clinical implications of these findings are the ability to stratify patients at high risk of posttransplant death by planning more intensive and accurate management for them.

On valid descriptive inference from non-probability sample

We examine the conditions under which descriptive inference can be based directly on theobserved distribution in a non-probability sample, under both the super-population and quasirandomisation modelling approaches. Review of existing estimation methods reveals that thetraditional formulation of these conditions may be inadequate due to potential issues of undercoverage or heterogeneous mean beyond the assumed model. We formulate unifying conditions that are applicable to both types of modelling approaches. The difficulties of empiricallyvalidating the required conditions are discussed, as well as valid inference approaches usingsupplementary probability sampling. The key message is that probability sampling may still benecessary in some situations, in order to ensure the validity of descriptive inference, but it can bemuch less resource-demanding given the presence of a big non-probability sample.

Impact of sufficient dimension reduction in nonparametric estimation of causal effect

We consider the estimation of causal treatment effect using nonparametric regression orinverse propensity weighting together with sufficient dimension reduction for searching lowdimensional covariate subsets. A special case of this problem is the estimation of a responseeffect with data having ignorable missing response values. An issue that is not well addressedin the literature is whether the estimation of the low-dimensional covariate subsets by sufficient dimension reduction has an impact on the asymptotic variance of the resulting causaleffect estimator. With some incorrect or inaccurate statements, many researchers believe thatthe estimation of the low-dimensional covariate subsets by sufficient dimension reduction doesnot affect the asymptotic variance. We rigorously establish a result showing that this is nottrue unless the low-dimensional covariate subsets include some covariates superfluous for estimation, and including such covariates loses efficiency. Our theory is supplemented by somesimulation results.