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BACKGROUND: Ileal neuroendocrine tumors (i-NETs) are characterized by their multifocality and bulky mesenteric mass. Having shown that minimally invasive surgery (MIS) utilizing a hand-access port device has favorable short-term outcomes and achieves the goals of surgery for i-NETs, we sought to analyze long-term survival outcomes of MIS. METHODS: One hundred and sixty-eight patients who underwent resection of primary i-NETs at a single institution between January 2007 and February 2023 were retrospectively studied. Patients were categorized into the MIS or open surgery cohorts on an intention-to-treat basis. Open surgery was selected mainly based on the need for hepatectomy or bulky mesenteric mass resection. Overall survival was analyzed using log-rank tests with propensity score matching (PSM) and Cox proportional hazards regression. PSM was performed to reduce standardized mean differences of the variables to

PURPOSE: The retrograde femoral nailing advanced (RFNA) system (DePuy synthes) is a commonly used implant for the fixation of low distal femur and periprosthetic fractures. There is concern that the rate of distal interlock screw back-out may be higher for the RFNA compared to other nails (ON). The purpose of this study was to evaluate the incidence of interlock screw back-out and associated screw removal for RFNA versus ON, along with associated risk factors. METHODS: A retrospective comparative study of patients who underwent retrograde nailing for a distal femur fracture at an academic level one trauma center was performed. The incidence of distal interlock screw back-out and need for screw removal were compared for RFNA versus a propensity score matched cohort who received other nails. RESULTS: One hundred and ten patients underwent retrograde nailing with the RFNA for a distal femur fracture from 2015 to 2022 (average age: 66, BMI: 32, 52.7% smokers, 54.5% female, 61.8%). There was a significantly higher rate of interlock back-out in the RFNA group compared to the ON (27 patients, 24.5% vs 12 patients, 10.9%, p = 0.01), which occurred 6.3 weeks postoperatively. Screw removal rates for back-out were not significantly different for the RFNA group versus ON (8 patients, 7.3% vs 3 patients, 2.7%, p = 0.12). CONCLUSION: In this retrospective comparative study of distal femur fractures treated with retrograde nailing, the RFNA implant was associated with an increased risk of distal interlock screw back-out compared to other nails.

OBJECTIVES: To describe the utilization of early ketamine use among patients mechanically ventilated for COVID-19, and examine associations with in-hospital mortality and other clinical outcomes. DESIGN: Retrospective cohort study. SETTING: Six hundred ten hospitals contributing data to the Premier Healthcare Database between April 2020 and June 2021. PATIENTS: Adults with COVID-19 and greater than or equal to 2 consecutive days of mechanical ventilation within 5 days of hospitalization. INTERVENTION: The exposures were early ketamine use initiated within 2 days of intubation and continued for greater than 1 day. MEASUREMENTS: Primary was hospital mortality. Secondary outcomes included length of stay (LOS) in the hospital and ICUs, ventilator days, vasopressor days, renal replacement therapy (RRT), and total hospital cost. The propensity score matching analysis was used to adjust for confounders. MAIN RESULTS: Among 42,954 patients, 1,423 (3.3%) were exposed to early ketamine use. After propensity score matching including 1,390 patients in each group, recipients of ketamine infusions were associated with higher hospital mortality (52.5% vs. 45.9%, risk ratio: 1.14, [1.06-1.23]), longer median ICU stay (13 vs. 12 d, mean ratio [MR]: 1.15 [1.08-1.23]), and longer ventilator days (12 vs. 11 d, MR: 1.19 [1.12-1.27]). There were no associations for hospital LOS (17 [10-27] vs. 17 [9-28], MR: 1.05 [0.99-1.12]), vasopressor days (4 vs. 4, MR: 1.04 [0.95-1.14]), and RRT (22.9% vs. 21.7%, RR: 1.05 [0.92-1.21]). Total hospital cost was higher (median $72,481 vs. $65,584, MR: 1.11 [1.05-1.19]). CONCLUSIONS: In a diverse sample of U.S. hospitals, about one in 30 patients mechanically ventilated with COVID-19 received ketamine infusions. Early ketamine may have an association with higher hospital mortality, increased total cost, ICU stay, and ventilator days, but no associations for hospital LOS, vasopressor days, and RRT. However, confounding by the severity of illness might occur due to higher extracorporeal membrane oxygenation and RRT use in the ketamine group. Further randomized trials are needed to better understand the role of ketamine infusions in the management of critically ill patients.

BACKGROUND: Multidisciplinary heart team (HT) evaluation is recommended for patients with severe primary mitral regurgitation to optimize treatment decisions. However, its impact on patient outcomes remains unknown. We evaluated the impact of implementing mitral HT on patient survival. METHODS AND RESULTS: We conducted a retrospective cohort study of patients with new diagnoses of severe primary mitral regurgitation in a large healthcare network echocardiogram database between 2016 and 2020. We compared the incidence of multidisciplinary evaluation by structural cardiology and cardiac surgery services and 2-year survival before and after mitral HT implementation. The 1:1 propensity-score matching between pre- and post-mitral HT used Society of Thoracic Surgeons Predicted Risk of Mortality for mitral repair, age, sex, race, heart failure symptoms, inpatient setting, history of MI, and dementia as covariates. Logistic regression identified variables associated with the likelihood of undergoing multidisciplinary evaluation. Among 70 510 echocardiograms performed, 391 patients had severe primary mitral regurgitation (median age, 77 years; 46% women). Multidisciplinary evaluation increased from 29% to 89% (P

ObjectivesVedolizumab (VDZ) and ustekinumab (UST) are second-line treatments in pediatric patients with ulcerative colitis (UC) refractory to antitumor necrosis factor (anti-TNF) therapy. Pediatric studies comparing the effectiveness of these medications are lacking. Using a registry from ImproveCareNow (ICN), a global research network in pediatric inflammatory bowel disease, we compared the effectiveness of UST and VDZ in anti-TNF refractory UC.MethodsWe performed a propensity-score weighted regression analysis to compare corticosteroid-free clinical remission (CFCR) at 6 months from starting second-line therapy. Sensitivity analyses tested the robustness of our findings to different ways of handling missing outcome data. Secondary analyses evaluated alternative proxies of response and infection risk.ResultsOur cohort included 262 patients on VDZ and 74 patients on UST. At baseline, the two groups differed on their mean pediatric UC activity index (PUCAI) (p = 0.03) but were otherwise similar. At Month 6, 28.3% of patients on VDZ and 25.8% of those on UST achieved CFCR (p = 0.76). Our primary model showed no difference in CFCR (odds ratio: 0.81; 95% confidence interval [CI]: 0.41-1.59) (p = 0.54). The time to biologic discontinuation was similar in both groups (hazard ratio: 1.26; 95% CI: 0.76-2.08) (p = 0.36), with the reference group being VDZ, and we found no differences in clinical response, growth parameters, hospitalizations, surgeries, infections, or malignancy risk. Sensitivity analyses supported these findings of similar effectiveness.ConclusionsUST and VDZ are similarly effective for inducing clinical remission in anti-TNF refractory UC in pediatric patients. Providers should consider safety, tolerability, cost, and comorbidities when deciding between these therapies.

BackgroundThis study evaluated the real-world impact of acupuncture on analgesics and healthcare resource utilization among breast cancer survivors.MethodsFrom a United States (US) commercial claims database (25% random sample of IQVIA PharMetrics® Plus for Academics), we selected 18-63 years old malignant breast cancer survivors experiencing pain and ≥ 1 year removed from cancer diagnosis. Using the difference-in-difference technique, annualized changes in analgesics [prevalence, rates of short-term (

PURPOSE: Long-term survival in high-risk neuroblastoma (HRNB) is approximately 50%, with mortality primarily driven by relapse. Eflornithine (DFMO) to reduce risk of relapse after completion of immunotherapy was investigated previously in a single-arm, phase II study (NMTRC003B; ClinicalTrials.gov identifier: NCT02395666) that suggested improved event-free survival (EFS) and overall survival (OS) compared with historical rates in a phase III trial (Children Oncology Group ANBL0032; ClinicalTrials.gov identifier: NCT00026312). Using patient-level data from ANBL0032 as an external control, we present new analyses to further evaluate DFMO as HRNB postimmunotherapy maintenance. PATIENTS AND METHODS: NMTRC003B (2012-2016) enrolled patients with HRNB (N = 141) after standard up-front or refractory/relapse treatment who received up to 2 years of continuous treatment with oral DFMO (750 ± 250 mg/m2 twice a day). ANBL0032 (2001-2015) enrolled patients with HRNB postconsolidation, 1,328 of whom were assigned to dinutuximab (ch.14.18) treatment. Selection rules identified 92 NMTRC003B patients who participated in (n = 87) or received up-front treatment consistent with (n = 5) ANBL0032 (the DFMO/treated group) and 852 patients from ANBL0032 who could have been eligible for NMTRC003B after immunotherapy, but did not enroll (the NO-DFMO/control group). The median follow-up time for DFMO/treated patients was 6.1 years (IQR, 5.2-7.2) versus 5.0 years (IQR, 3.5-7.0) for NO-DFMO/control patients. Kaplan-Meier and Cox regression compared EFS and OS for overall groups, 3:1 (NO-DFMO:DFMO) propensity score-matched cohorts balanced on 11 baseline demographic and disease characteristics with exact matching on MYCN, and additional sensitivity analyses. RESULTS: DFMO after completion of immunotherapy was associated with improved EFS (hazard ratio [HR], 0.50 [95% CI, 0.29 to 0.84]; P = .008) and OS (HR, 0.38 [95% CI, 0.19 to 0.76]; P = .007). The results were confirmed with propensity score-matched cohorts and sensitivity analyses. CONCLUSION: The externally controlled analyses presented show a relapse risk reduction in patients with HRNB treated with postimmunotherapy DFMO.

Background: Large type 3 (diameter ≥ 8 cm) and type 4 gastric cancers have been arbitrarily combined in Japan as a single entity. However, whether these two types are oncologically similar remain unclear. This study aimed to clarify this issue. Methods: In this retrospective study, we analyzed a database of 3,575 patients from nine institutions who underwent gastrectomy between 2010 and 2014. Using propensity scores to balance significant variables, we compared prognoses and tumor recurrences. Results: Of patients with clinical T3/T4 who underwent R0 resection, 75 and 73 had large type 3 and 4 tumors, respectively. Patients with type 4 tumors had significantly lower overall survival rates than those of patients with large type 3 tumors (hazard ratio [HR] 1.77; 95% confidence interval [CI] 1.14–2.74). However, among the large type 3 tumors, a remarkable difference in prognosis was observed between the differentiated and undifferentiated histological types. A comparison was made between large type 3 with undifferentiated phenotype and type 4, each with 39 patients after propensity score matching. Outcomes in both groups were similar in terms of overall survival (HR 1.28; 95% CI 0.73–2.25) and relapse-free survival (HR 1.34; 95% CI 0.80–2.27). No statistically significant differences were observed in the incidence of peritoneal recurrence (35.9% vs. 46.1%, P = 0.36) and lymph node recurrence (25.6% vs. 12.8%, P = 0.15). Conclusions: Large type 3 tumors with undifferentiated phenotype and type 4 tumors were oncologically similar. This subgroup could be considered as a new entity for future clinical trials. [ABSTRACT FROM AUTHOR]

Objective: This study aimed to investigate the characteristics of patients with colorectal mucinous adenocarcinoma (MAC) who benefit from postoperative chemotherapy (POCT) and to develop effective postoperative survival nomograms for predicting overall survival (OS) in colorectal MAC patients. Methods: Data of colorectal MAC patients who underwent surgery from the Surveillance, Epidemiology, and End Results (SEER) database between 2010 and 2020 were collected. Patients were grouped based on POCT, and intergroup analysis was performed using 1:1 propensity score matching (PSM). Kaplan-Meier (K-M) curves were used to compare the prognosis between the 2 groups. Cox analysis was employed to identify factors associated with OS in patients with colorectal MAC who underwent POCT. The variance inflation factor (VIF) and bilateral stepwise regression were used to determine factors included in the model. Additionally, a nomogram was constructed to predict postoperative survival outcomes for patients. The discriminative ability of the nomograms was evaluated using the C-index and calibration curve analysis, the decision curve analysis (DCA) assessed the clinical utility of the nomogram, and the receiver operating characteristic (ROC) curve evaluated the nomograms' performance. Results: This study encompassed 6829 patients with colorectal MAC, among whom 2258 received POCT, and 4571 did not. Whether pre or post PSM, patients in the POCT group consistently exhibited a superior median OS compared to those in the postoperative non-chemotherapy group (P < .0001). For colorectal MAC patients undergoing POCT, OS was correlated with factors such as patient age, carcinoembryonic antigen levels, tumor deposits, perineural invasion (PNI), lymph node examination count, T staging, and Grade staging. Notably, a significant chemotherapy advantage was observed in patients without perineural invasion, those with lymph node examination counts exceeding 12, and patients with moderately differentiated tumors. The overall colorectal MAC patient postoperative OS predictive nomogram demonstrated a C-index of .74, with a calibration curve near the diagonal and a DCA curve indicating positive net benefits. In comparison to TNM staging, the ROC curves of the nomogram at 1 year, 3 years, and 5 years demonstrated superior predictive capabilities (AUC: .80 vs .71, .78 vs .71, .77 vs .70). Conclusion: This study revealed the characteristics of colorectal MAC patients who benefit from POCT and established effective prognostic nomograms, which can aid clinicians in designing personalized treatment plans for individual patients and promote precision medicine. [ABSTRACT FROM AUTHOR]

Postpartum depression is a major psychiatric disorder associated with maternal suicide and child developmental disturbances. In this study, we aimed to investigate whether general anesthesia for cesarean delivery is associated with a higher rate of new-onset depression after delivery than neuraxial anesthesia. This is a nationwide retrospective cohort study using data retrieved from the National Health Insurance Research Database between 2014 and 2018. Women who had cesarean delivery under general or neuraxial anesthesia were enrolled. After 1:4 propensity score matching, there were 4544 and 18,176 women under the general and neuraxial anesthesia groups, respectively. The primary outcome was new-onset depression diagnosed after delivery in a time-to-event analysis setting. After propensity-score matching, the rate of new-onset depression diagnosed within 1 year was 1.10 % (50/4488) and 0.86 % (157/18176) after cesarean delivery under general and neuraxial anesthesia, respectively. For depression diagnosed within 90 days of delivery, significant difference between the two groups was noted (0.51 % vs. 0.30 %, P = 0.031). In the time-to-event analysis with Cox regression model, women who delivered under general anesthesia were associated with significantly higher risk of postpartum depression within 90 days (Hazard ratio [HR], 1.71; 95 % CI, 1.05–2.79) compared with those under neuraxial anesthesia. The observational design only allows asserting association, rather than establishing causality between exposure and outcomes. Women who underwent cesarean delivery under general anesthesia had a higher risk of subsequent depression within 90 days than those under neuraxial anesthesia. Early screening for depressive disorders might facilitate timely management. [Display omitted] • Postpartum depression may lead to serious consequences, including maternal suicide. • Effect of neuraxial and general anesthesia on subsequent depression was compared. • C-section under general anesthesia may be associated with subsequent depression. [ABSTRACT FROM AUTHOR]

This study sought to examine the effect of antithrombotic use on clinical outcomes in non-variceal upper gastrointestinal bleeding (UGIB). Patients consecutively diagnosed with non-variceal UGIB between February 2019 and September 2020 were divided into two groups based on their antithrombotic use: users and non-users. Using propensity score matching (PSM) and multivariable regression analyses, the impact of antithrombotic use prior to UGIB presentation on clinical outcomes was examined. In the entire cohort, there were 210 and 260 patients in the antithrombotic user and non-user groups, respectively. Using PSM analysis with seven covariates, two matched groups of 157 patients were created at a 1:1 ratio. In the matched cohort, despite their longer hospital stays and a higher rate of intensive care unit admissions, the patients in the user group had lower 30- and 90-day mortality rates (4.5% vs. 14.0 %; p = 0.003 and 8.9% vs. 18.5 %; p = 0.014, respectively). In the entire cohort, multivariable analyses adjusted for confounding factors revealed that antithrombotic use was associated with lower risks of in-hospital (adjusted OR: 0.437; 95 % CI: 0.191–0.999), 30-day (adjusted OR: 0.261; 95 % CI: 0.099–0.689), and 90-day (adjusted OR: 0.386; 95 % CI: 0.182–0.821) mortality. Antithrombotic use prior to UGIB presentation was found to be an independent protective factor for all-cause mortality. [ABSTRACT FROM AUTHOR]

Background: Previous research has shown that the use of renin-angiotensin system (RAS) blockers is linked to a lower prevalence of posttraumatic stress disorder (PTSD), but longitudinal studies are scarce. We aimed to estimate the association between the use of RAS blockers and the risk of PTSD among individuals taking antihypertensive medications. Methods: This longitudinal study included participants aged 40–69 from the UK Biobank. Exposure data were obtained from the initial assessment (2006–10), while outcome data were obtained from the online mental health questionnaire administered 6–11 years later (2016–17). We included participants who were under antihypertensive treatment and did not have a prior diagnosis of PTSD before the initial assessment. Use of RAS blockers was defined as self-reported regular use, at the initial assessment, of angiotensin-converting enzyme inhibitor (ACEi) or angiotensin receptor blocker (ARB). Among participants who experienced adverse life experiences, cases of probable PTSD were defined with the six-item PTSD Checklist-Civilian version score ≥ 14. Logistic regression with inverse probability of treatment weighting was used to estimate the odds ratios (ORs) and 95% confidence interval (CI) for the association between RAS blocker use and the risk of probable PTSD. Results: Of the 15,954 participants (mean age = 59.9 years; 42.6% women) under antihypertensive treatment with no prior history of PTSD at the initial assessment, 64.5% were taking RAS blockers. After a mean follow-up of 7.5 years, 1,249 (7.8%) were newly identified with probable PTSD. RAS blocker users had a lower risk of probable PTSD than RAS blocker non-users (OR = 0.84 [95% CI: 0.75–0.94]), whereas the use of other antihypertensive medications showed no such association (users vs. non-users; calcium channel blockers, OR = 0.99 [95% CI: 0.88–1.11]; beta-blockers, 1.20 [1.08–1.34]; and thiazide-related diuretics, 1.15 [1.03–1.29]). The association between probable PTSD risk and the use of ACEi vs. ARB showed no significant difference (p = 0.96). Conclusions: Among individuals under antihypertensive treatment, the use of RAS blockers was associated with a decreased risk of probable PTSD. This added benefit of RAS blockers should be considered in the selection of antihypertensive medications. [ABSTRACT FROM AUTHOR]

ABSTRACT Objectives Methods Results Conclusions The 24‐month, prospective, non‐interventional, European multicenter A‐SURE study evaluated the real‐world effectiveness of prophylaxis using an extended half‐life recombinant factor VIII (FVIII) Fc fusion protein, efmoroctocog alfa (hereinafter rFVIIIFc), compared with prophylaxis using standard half‐life (SHL) FVIII products in patients with hemophilia A.Primary endpoints were annualized bleeding rate (ABR), annualized injection frequency, and annualized factor consumption. A comparative study design unique for an observational hemophilia study was implemented to reduce potential confounding in effectiveness estimates, wherein each patient prescribed rFVIIIFc was matched with one receiving SHL FVIII. Propensity scores were used for adjustment in statistical analyses.Outcomes for all primary endpoints were significantly better in the rFVIIIFc group (n = 184) compared with the SHL FVIII group (n = 170): mean ABR 1.5 versus 2.3 (difference of −0.8; p = 0.0147); mean annualized injection frequency 114.4 versus 169.2 (difference of −54.8; p < 0.0001); and mean annualized factor consumption 243 024.2 versus 288 718.6 International Units (difference of 45 694.5; p = 0.0003). rFVIIIFc was well tolerated, with no inhibitor development.rFVIIIFc has superior prophylactic effectiveness versus SHL FVIII, providing higher bleed protection with fewer injections and lower factor consumption. [ABSTRACT FROM AUTHOR]

Aim: To evaluate the efficacy of the PerioRisk prognostic tool in implementing the effect of motivational interviewing (MI) on psychological outcomes and supragingival plaque control. Materials and Methods: Participants underwent MI immediately after their first periodontal visit. According to a parallel‐arm, randomized study design, MI was performed with (RISK group) or without (CTR group) information on PerioRisk level and treatment goals based on PerioRisk output. Psychological outcomes were assessed using the Positive Affect Negative Affect Scale (PANAS) and Protection Motivation Theory (PMT). Plaque index (PI) was re‐evaluated after 8–12 weeks. Results: Significant improvements in PMT overall score and PI were observed in CTR and RISK groups, without inter‐group difference in PANAS and PMT overall scores and PI. A sub‐analysis showed that the overall PMT scores recorded immediately after MI in both CTR and RISK groups for subjects with no tooth loss due to periodontitis were higher than those recorded before MI in subjects with tooth loss due to periodontitis. Conclusions: At first periodontal visit, MI (implemented with without PerioRisk) has tangible effects on psychological outcomes and supragingival plaque control and seems to anticipate the awareness that is commonly generated by periodontitis‐related tooth loss (ClinicalTrials.gov protocol registration ID: NCT05078411). [ABSTRACT FROM AUTHOR]

Multiple imputation (MI) is commonly implemented to mitigate potential selection bias due to missing data. The accompanying article by Nguyen and Stuart (Am J Epidemiol. 2024;193(10):1470-1476) examines the statistical consistency of several ways of integrating MI with propensity scores. As Nguyen and Stuart noted, variance estimation for these different approaches remains to be developed. One common option is the nonparametric bootstrap, which can provide valid inference when closed-form variance estimators are not available. However, there is no consensus on how to implement MI and nonparametric bootstrapping in analyses. To complement Nguyen and Stuart's article on MI and propensity score analyses, we review some currently available approaches on variance estimation with MI and nonparametric bootstrapping. [ABSTRACT FROM AUTHOR]

Objective: Malignant soft tissue sarcoma (MSTS) is a rare disease, but is seen in patients undergoing orthopedic surgery. Although the association of periodontal disease with various cancers occurring in the oral cavity, gastrointestinal tract, lungs, and prostate, has been reported, the association between periodontal disease and MSTS remains unclear. This study investigated the association between periodontal disease and MSTS in patients undergoing orthopedic surgery. Subjects and Methods: One hundred fifteen patients who underwent orthopedic surgery between 2017 and 2021 were retrospectively enrolled (mean age = 66.8 ± 10.7 years). The patient background was adjusted by the propensity score (PS). Subsequently, the association of periodontal disease with MSTS was analyzed using PS inverse probability of treatment weighting (IPTW). Periodontal status was determined by evaluating the periodontal inflamed surface area, which was calculated by measuring the periodontal probing pocket depth and detecting bleeding on probing. Results: Multivariate logistic regression analysis after adjustment by the PS showed that severe periodontitis was significantly associated with MSTS (odds ratio 2.81, p = 0.04). Furthermore, IPTW showed that severe periodontitis was significantly associated with MSTS (odds ratio 3.21, p = 0.01). Conclusion: The results indicate an association between periodontal inflammation and MSTS. [ABSTRACT FROM AUTHOR]

Background: Propensity scores (PS) are typically evaluated using balance metrics that focus on covariate balance, often without considering their predictive power for the outcome. This approach may not always result in optimal bias reduction in the treatment effect estimate. To address this issue, evaluating covariate balance through prognostic scores, which account for the relationship between covariates and the outcome, has been proposed. Similarly, using a typical model averaging approach for PS estimation that minimizes prediction error for treatment status and covariate imbalance does not necessarily optimize PS-based confounding adjustment. As an alternative approach, using the averaged PS model that minimizes inter-group differences in the prognostic score may further reduce bias in the treatment effect estimate. Moreover, since the prognostic score is also an estimated quantity, model averaging in the prognostic scores can help identify a better prognostic score model. Utilizing the model-averaged prognostic scores as the balance metric for constructing the averaged PS model can contribute to further decreasing bias in treatment effect estimates. This paper demonstrates the effectiveness of the PS model averaging approach based on prognostic score balance and proposes a method that uses the model-averaged prognostic score as a balance metric, evaluating its performance through simulations and empirical analysis. Methods: We conduct a series of simulations alongside an analysis of empirical observational data to compare the performances of weighted treatment effect estimates using the proposed and existing approaches. In our examination, we separately provid four candidate estimates for the PS and prognostic score models using traditional regression and machine learning methods. The model averaging of PS based on these candidate estimators is performed to either maximize the prediction accuracy of the treatment or to minimize intergroup differences in covariate distributions or prognostic scores. We also utilize not only the prognostic scores from each candidate model but also an averaged score that best predicted the outcome, for the balance assessment. Results: The simulation and empirical data analysis reveal that our proposed model-averaging approaches for PS estimation consistently yield lower bias and less variability in treatment effect estimates across various scenarios compared to existing methods. Specifically, using the optimally averaged prognostic scores as a balance metric significantly improves the robustness of the weighted treatment effect estimates. Discussion: The prognostic score-based model averaging approach for estimating PS can outperform existing model averaging methods. In particular, the estimator using the model averaging prognostic score as a balance metric can produce more robust estimates. Since our results are obtained under relatively simple conditions, applying them to real data analysis requires adjustments to obtain accurate estimates according to the complexity and dimensionality of the data. Conclusions: Using the prognostic score as the balance metric for the PS model averaging enhances the performance of the treatment effect estimator, which can be recommended for a wide variety of situations. When applying the proposed method to real-world data, it is important to use it in conjunction with techniques that mitigate issues arising from the complexity and high dimensionality of the data. [ABSTRACT FROM AUTHOR]

Background: Minimalist transcatheter aortic valve replacement (TAVR) under monitored anesthesia care (MAC) emphasizes early recovery. Remimazolam is a novel benzodiazepine with a short recovery time. This study hypothesized that remimazolam is non-inferior to dexmedetomidine in terms of recovery after TAVR. Methods: In this retrospective observational study, remimazolam was compared to dexmedetomidine in patients who underwent TAVR under MAC at a tertiary academic hospital between July 2020 and July 2022. The primary outcome was timely recovery after TAVR, defined as discharge from the intensive care unit within the first day following the procedure. Propensity score matching was used to compare timely recovery between remimazolam and dexmedetomidine, applying a non-inferiority margin of -10%. Results: The study included 464 patients, of whom 218 received remimazolam and 246 received dexmedetomidine. After propensity score matching, 164 patients in each group were included in the analysis. Regarding timely recovery after TAVR, remimazolam was non-inferior to dexmedetomidine (152 of 164 [92.7%] in the remimazolam group versus 153 of 164 [93.3%] in the dexmedetomidine group, risk difference [95% CI]: -0.6% [-6.7%, 5.5%]). The use of remimazolam was associated with fewer postoperative vasopressors/ inotropes (21 of 164 [12.8%] vs. 39 of 164 [23.8%]) and temporary pacemakers (TPMs) (76 of 164 [46.3%] vs. 108 of 164 [65.9%]) compared to dexmedetomidine. Conclusions: In patients undergoing TAVR under MAC, remimazolam was non-inferior to dexmedetomidine in terms of timely recovery. Remimazolam may be associated with better postoperative recovery profiles, including a lesser need for vasopressors/inotropes and TPMs. [ABSTRACT FROM AUTHOR]

Percutaneous mitral balloon commissurotomy (PMBC) is the standard treatment option for patients with rheumatic mitral stenosis (MS), according to current guidelines. This study aimed to compare the outcomes of rheumatic mitral valve repair (rMVR) and PMBC in this patient population. Baseline, clinical, and follow-up data from 703 patients with rheumatic heart disease who underwent PMBC or rMVR at the current centre were collected and analysed. A 1:1 propensity score (PS) matching method was used to balance the differences in baseline characteristics between the two groups. The primary outcome was mitral valve reoperation, and the secondary outcome was all-cause mortality. Propensity score matching generated 101 patient pairs for comparison. In the matched population, there were no significant differences in the early clinical outcomes between the groups. The median follow-up time was 40.9 months. Overall, patients in the rMVR group had a statistically significantly lower risk of mitral valve reoperation than those in the PMBC group (HR 0.186; 95% CI 0.041–0.835; p=0.028). Regarding all-cause mortality, no statistically significant differences were observed between the rMVR and PMBC groups (HR 4.065; 95% CI 0.454–36.374; p=0.210). Compared with PMBC, rMVR has more advantages for the correction of valve lesions; therefore, it may offer a better prognosis than PMBC in select patients with rheumatic MS. However, this finding needs to be verified in future studies with larger sample sizes and longer follow-up periods. [ABSTRACT FROM AUTHOR]

Active labour market policies for young people, backed by the EU Youth Guarantee programme, led governments to trying new measures, amongst them internships for graduates of different education levels. This youth, despite being better prepared than early school leavers, still face problems during their professional insertion processes. Faulty progressions can become 'lost in transition' situations (Brinton [2010]. Lost in Transition: Youth, Work, and Instability in Postindustrial Japan. Cambridge: Cambridge University Press.). This paper analyzes the effects in terms of employment outcomes of a programme with two types of internships: in private firms and in public administration offices. A counterfactual analysis is carried out by matching intern groups of either type with a control group formed from candidates who ended up without an internship. The results from a first analysis with registered data were enhanced by implementing a second analysis based upon survey data, which permitted the inclusion of family legacy variables in a medium-term analysis. Estimated effects are positive and significant for participants in firms' internships, and negative and significant for interns who worked in public organisations. Additionally, the survey uncovers that work contracts offered by firms after the programme play a positive role. The results should be very useful for decision making in terms of active measures addressing these issues for young people. [ABSTRACT FROM AUTHOR]

This article considers the estimation of quantile treatment effects under the assumption of unconfoundedness given quasi-experimental data. We propose a semiparametric single-index method to estimate the propensity score. Our approach overcomes the curse of dimensionality issue of a nonparametric propensity score and can handle a moderately large dimension of covariates. It is more flexible than the parametric propensity score and thereby alleviates the possible model misspecification problem. We derive the asymptotic distribution of the quantile treatment effect estimator that is based on the semiparametric propensity score. We also propose a consistent variance estimator and construct the confidence intervals for the QTE estimator. Monte Carlo simulation results show that the proposed estimator performs well in finite samples and the confidence intervals have adequate coverage rates. We demonstrate the usefulness of our method by applying it to a study of the quantile treatment effects of college education on income. [ABSTRACT FROM AUTHOR]

Previous studies suggest that Dupuytren's disease is associated with increased mortality, but most studies failed to account for important confounders. In this population-based cohort study, general practitioners' (GP) data were linked to Statistics Netherlands to register all-cause and disease-specific mortality. Patients with Dupuytren's disease were identified using the corresponding diagnosis code and assessing free-text fields from GP consultations. Multiple imputations were performed to estimate missing values of covariates, followed by 1:7 propensity score matching to balance cases with controls on confounding factors. A frailty proportional hazard model was used to compare mortality between both groups. Out of 209,966 individuals, 2561 patients with Dupuytren's disease were identified and matched to at least four controls. After a median follow-up of 5 years, mortality was found to be actually reduced in patients with Dupuytren's disease. There was no difference in mortality secondary to cancer or cardiovascular disease. Future studies with longer average follow-up using longitudinal data should clarify these associations in the longer term. Level of evidence: III [ABSTRACT FROM AUTHOR]

Background: COVID-19 vaccines effectively prevent infection and hospitalization. However, few population-based studies have compared the clinical characteristics and outcomes of patients hospitalized for COVID-19 using advanced statistical methods. Our objective is to address this evidence gap by comparing vaccinated and unvaccinated patients hospitalized for COVID-19. Methods: This retrospective cohort included adult COVID-19 patients admitted from March 2021 to August 2022 from 27 hospitals. Clinical characteristics, vaccination status, and outcomes were extracted from medical records. Vaccinated and unvaccinated patients were compared using propensity score analyses, calculated based on variables associated with vaccination status and/or outcomes, including waves. The vaccination effect was also assessed by covariate adjustment and feature importance by permutation. Results: From the 3,188 patients, 1,963 (61.6%) were unvaccinated and 1,225 (38.4%) were fully vaccinated. Among these, 558 vaccinated individuals were matched with 558 unvaccinated ones. Vaccinated patients had lower rates of mortality (19.4% vs. 33.3%), invasive mechanical ventilation (IMV-18.3% vs. 34.6%), noninvasive mechanical ventilation (NIMV-10.6% vs. 22.0%), intensive care unit admission (ICU-32.0% vs. 44.1%) vasoactive drug use (21.1% vs. 32.6%), dialysis (8.2% vs. 14.7%) hospital length of stay (7.0 vs. 9.0 days), and thromboembolic events (3.9% vs.7.7%), p < 0.05 for all. Risk-adjusted multivariate analysis demonstrated a significant inverse association between vaccination and in-hospital mortality (adjusted odds ratio [aOR] = 0.42, 95% confidence interval [CI]: 0.31–0.56; p < 0.001) as well as IMV (aOR = 0.40, 95% CI: 0.30–0.53; p < 0.001). These results were consistent in all analyses, including feature importance by permutation. Conclusion: Vaccinated patients admitted to hospital with COVID-19 had significantly lower mortality and other severe outcomes than unvaccinated ones during the Delta and Omicron waves. These findings have important implications for public health strategies and support the critical importance of vaccination efforts, particularly in low-income countries, where vaccination coverage remains suboptimal. [ABSTRACT FROM AUTHOR]

Parkinson’s disease (PD) reports high rates of morbidity and mortality, but the risk of adverse cardiovascular outcomes in patients with PD has not been fully elucidated. This bi-center retrospective cohort study using the electronic health records (EHR) database of two tertiary hospitals screened a total of 327,292 subjects who visited the outpatient clinic, and 1194 patients with PD were propensity score-matched with a control population. The primary outcome was the occurrence of major adverse cardiovascular events (MACE). Key secondary outcomes included all-cause death, cardiovascular (CV) death, stroke, myocardial infarction (MI), heart failure hospitalization and 30-day CV death. After PS matching, MACE occurrence was not significantly different between PD and non-PD groups (18.2% vs. 17.5%, log-rank p = 0.98). Key secondary outcomes were also similar between the two groups. In patients with PD, MACE rate, and also CV risk score, were higher in patients with more severe PD (according to Hoehn and Yahr scale and unified Parkinson’s disease rating scale), and after multivariable analysis, PD severity was not an independent predictor of MACE. Patients with PD are at an increased risk of adverse cardiovascular outcomes, but the contribution from other common CV risk factors cannot be ignored. The management of prevalent CV risk factors is therefore important in mitigating adverse outcomes among patients with PD. [ABSTRACT FROM AUTHOR]

In settings requiring synthetic data generation based on a clinical cohort, e.g., due to data protection regulations, heterogeneity across individuals might be a nuisance that we need to control or faithfully preserve. The sources of such heterogeneity might be known, e.g., as indicated by sub-groups labels, or might be unknown and thus reflected only in properties of distributions, such as bimodality or skewness. We investigate how such heterogeneity can be preserved and controlled when obtaining synthetic data from variational autoencoders (VAEs), i.e., a generative deep learning technique that utilizes a low-dimensional latent representation. To faithfully reproduce unknown heterogeneity reflected in marginal distributions, we propose to combine VAEs with pre-transformations. For dealing with known heterogeneity due to sub-groups, we complement VAEs with models for group membership, specifically from propensity score regression. The evaluation is performed with a realistic simulation design that features sub-groups and challenging marginal distributions. The proposed approach faithfully recovers the latter, compared to synthetic data approaches that focus purely on marginal distributions. Propensity scores add complementary information, e.g., when visualized in the latent space, and enable sampling of synthetic data with or without sub-group specific characteristics. We also illustrate the proposed approach with real data from an international stroke trial that exhibits considerable distribution differences between study sites, in addition to bimodality. These results indicate that describing heterogeneity by statistical approaches, such as propensity score regression, might be more generally useful for complementing generative deep learning for obtaining synthetic data that faithfully reflects structure from clinical cohorts. [ABSTRACT FROM AUTHOR]

Background: The potential adverse effects associated with invasive mechanical ventilation (MV) can lead to delayed decisions on starting MV. We aimed to explore the association between the timing of MV and the clinical outcomes in patients with sepsis ventilated in intensive care unit (ICU). Methods: We analyzed data of adult patients with sepsis between September 2019 and December 2021. Data was collected through the Korean Sepsis Alliance from 20 hospitals in Korea. Patients who were admitted to ICU and received MV were included in the study. Patients were divided into 'early MV' and 'delayed MV' groups based on whether they were on MV on the first day of ICU admission or later. Propensity score matching was applied, and patients in the two groups were compared on a 1:1 ratio to overcome bias between the groups. Outcomes including ICU mortality, hospital mortality, length of hospital and ICU stay, and organ failure at ICU discharge were compared. Results: Out of 2440 patients on MV during ICU stay, 2119 'early MV' and 321 'delayed MV' cases were analyzed. The propensity score matching identified 295 patients in each group with similar baseline characteristics. ICU mortality was lower in 'early MV' group than 'delayed MV' group (36.3% vs. 46.4%; odds ratio, 0.66; 95% confidence interval, 0.47–0.93; p = 0.015). 'Early MV' group had lower in-hospital mortality, shorter ICU stay, and required tracheostomy less frequently than 'delayed MV' group. Multivariable logistic regression model identified 'early MV' as associated with lower ICU mortality (odds ratio, 0.38; 95% confidence interval, 0.29–0.50; p < 0.001). Conclusion: In patients with sepsis ventilated in ICU, earlier start (first day of ICU admission) of MV may be associated with lower mortality. [ABSTRACT FROM AUTHOR]

AbstractThis article builds on the Empirical Monte Carlo simulation approach to study the estimation of Timing-of-Events (ToE) models. We exploit rich Swedish data of unemployed job seekers with information on participation in a training program to simulate placebo treatment durations. We first use these simulations to examine which covariates are key confounders to be included in dynamic selection models for training participation. The joint inclusion of specific short-term employment history indicators (notably, the share of time spent in employment), together with baseline socio-economic characteristics, regional and inflow timing information, is important to deal with selection bias. Next, we omit subsets of explanatory variables and estimate ToE models with discrete distributions for the ensuing systematic unobserved heterogeneity. In many cases, the ToE approach provides accurate effect estimates, especially if time-varying variation in the unemployment rate of the local labor market is taken into account. However, assuming too many or too few support points for unobserved heterogeneity may lead to large biases. Information criteria, in particular those penalizing parameter abundance, are useful to select the number of support points. A comparison with other duration models shows that a Stratified Cox model performs well with abundant multiple spells but less well when multiple spells are uncommon. The standard Cox regression model performs poorly in all configurations as it is unable to account for unobserved heterogeneity. [ABSTRACT FROM AUTHOR]

ABSTRACT The innovative use of real‐world data (RWD) can answer questions that cannot be addressed using data from randomized clinical trials (RCTs). While the sponsors of RCTs have a central database containing all individual patient data (IPD) collected from trials, analysts of RWD face a challenge: regulations on patient privacy make access to IPD from all regions logistically prohibitive. In this research, we propose a double inverse probability weighting (DIPW) approach for the analysis sponsor to estimate the population average treatment effect (PATE) for a target population without the need to access IPD. One probability weighting is for achieving comparable distributions in confounders across treatment groups; another probability weighting is for generalizing the result from a subpopulation of patients who have data on the endpoint to the whole target population. The likelihood expressions for propensity scores and the DIPW estimator of the PATE can be written to only rely on regional summary statistics that do not require IPD. Our approach hinges upon the positivity and conditional independency assumptions, prerequisites to most RWD analysis approaches. Simulations are conducted to compare the performances of the proposed method against a modified meta‐analysis and a regular meta‐analysis. [ABSTRACT FROM AUTHOR]

Overlap weighting, using weights defined as the probability of receiving the opposite treatment, is a relatively new, alternative propensity score-based weighting technique used to adjust for confounding when estimating causal treatment effects. It has preferable properties compared to inverse probability of treatment weighting, such as exact covariate balance, safeguards against extreme weights and emphasis on medical equipoise, where treatment decisions are most uncertain. In this article, we introduce the overlap weighting methodology, compare it to inverse probability of treatment weighting and provide some strategies for assessing weighting impact, through an applied example of hospital mortality. When the propensity score distributions have large separation, inverse probability of treatment weighting has been shown to produce biased and less efficient estimates of the treatment effect, making overlap weighting a preferred method in such cases. [ABSTRACT FROM AUTHOR]

Causal inference for extreme events has many potential applications in fields such as climate science, medicine, and economics. We study the extremal quantile treatment effect of a binary treatment on a continuous, heavy-tailed outcome. Existing methods are limited to the case where the quantile of interest is within the range of the observations. For applications in risk assessment, however, the most relevant cases relate to extremal quantiles that go beyond the data range. We introduce an estimator of the extremal quantile treatment effect that relies on asymptotic tail approximation, and use a new causal Hill estimator for the extreme value indices of potential outcome distributions. We establish asymptotic normality of the estimators and propose a consistent variance estimator to achieve valid statistical inference. We illustrate the performance of our method in simulation studies, and apply it to a real dataset to estimate the extremal quantile treatment effect of college education on wage. for this article are available online. [ABSTRACT FROM AUTHOR]

Background: The complex work of public health nurses (PHNs) specifically related to mental health assessment, intervention, and outcomes makes it difficult to quantify and evaluate the improvement in client outcomes attributable to their interventions. Objectives: We examined heterogeneity across parents of infants served by PHNs receiving different interventions, compared the ability of traditional propensity scoringmethods versus energy-balancing weight (EBW) techniques to adjust for the complex and stark differences in baseline characteristics among those receiving different interventions, and evaluated the causal effects of the quantity and variety of PHN interventions on client health and social outcomes. Methods: This retrospective study of 4,109 clients used existing Omaha System data generated during the routine documentation of PHNhome visit data. We estimated the effects of intervention by computing and comparing weighted averages of the outcomes within the different treatment groups using two weighting methods: (a) inverse probability of treatment (propensity score) weighting and (b) EBWs. Results: Clients served by PHNs differed in baseline characteristics with clients with more signs/symptoms. Both weighting methods reduced heterogeneity in the sample. EBWs were more effective than inverse probability of treatment weighting in adjusting for multifaceted confounding and resulted in close balance of 105 baseline characteristics. Weighting the sample changed outcome patterns, especially when using EBWs. Clients who received more PHN interventions and a wider variety of them had improved knowledge, behavior, and status outcomes with no plateau over time, whereas the unweighted sample showed plateaus in outcomes over the course of home-visiting services. Discussion: Causal analysis of PHN-generated data demonstrated PHN intervention effectiveness for clients with mental health signs/symptoms. EBWs are a promising tool for evaluating the true causal effect of PHN home-visiting interventions. [ABSTRACT FROM AUTHOR]

With the rapid spread of telecommuting since the COVID-19 pandemic, companies have been relocating to high-end business centers, thereby enhancing the workplace experience for employees, a phenomenon called the "flight to quality." However, this trend's impact on rents and vacancy rates in individual office buildings has not been extensively studied. To determine the effects of the flight-to-quality phenomenon on individual buildings, we examine the impact of modern amenities, which directly influence employee lifestyles, on rents and vacancy rates in the Tokyo office market. Using a propensity score-based quasi-experimental method, we find that commercial properties with such modern amenities command higher rents and experience lower vacancy rates than those without. The difference in vacancy rates has increased since 2020. However, the significance of these amenities diminishes for properties less competitive in age, size, and location. The results indicate that the "flight to quality" may further polarize the office real estate market into two categories: one for high-end buildings experiencing increasing demand, and another for those with modest amenities experiencing decreasing demand. The findings have implications for office building owners/investors and the government, make educated decisions as to whether to invest in modern amenities, join the quality competition, or encourage urban restructuring. [ABSTRACT FROM AUTHOR]

Background: Ejaculated spermatozoa are considered to possess a higher fertilisation potential than testicular spermatozoa. In selected cases, the use of testicular spermatozoa from non‐azoospermic infertile men resulted in a higher implantation and pregnancy rate than the use of ejaculated spermatozoa. Objective: The primary objective was to compare the live birth rate and cumulative live birth rate between couples with failed intracytoplasmic sperm injection procedure using ejaculated spermatozoa who subsequently had an intracytoplasmic sperm injection cycle with testicular spermatozoa and those who subsequently had an intracytoplasmic sperm injection cycle with ejaculated spermatozoa. The secondary objective was to determine the indications for the use of testicular spermatozoa after intracytoplasmic sperm injection failure with ejaculated spermatozoa. Materials and methods: A retrospective study of matched couples using propensity score matching analysis was performed. After an intracytoplasmic sperm injection failure (cycle_1), intracytoplasmic sperm injection with either ejaculated spermatozoa (ejaculated sperm group), or testicular spermatozoa (testicular sperm group), was performed (cycle_2). The matching was on intracytoplasmic sperm injection performed in cycle_1 according to spermatozoa used (testicular or ejaculated) in cycle_2. Logistic regression was used to evaluate the influence of sperm origin on cumulative live birth rate. Univariate analysis on parameters of cycle_1 was used to identify the prognostic factors to propose an intracytoplasmic sperm injection with testicular spermatozoa in case of cycle_1 failure. The study outcomes were live birth rate and cumulative live birth rate. Results: Among the 6034 couples available, 63 were selected to constitute the testicular sperm group and 63 were selected by propensity score matching to constitute the ejaculated sperm group. After matching, the DNA fragmentation index was higher in the testicular sperm group (13.43% ± 9.65% vs. 8.93% ± 4.47%, p = 0.013); no significant difference was observed for the fertilisation rate, the number of obtained embryos, blastulation rate and frozen embryo rate. In cycle_2, the live birth rate was higher in the testicular group (22.2% vs. 0.0%, p < 0.001), as was the cumulative live birth rate (25.4% vs. 6.3%, p = 0.065). The prognostic factors identified for the proposal of intracytoplasmic sperm injection procedure with testicular spermatozoa after intracytoplasmic sperm injection failure with ejaculated spermatozoa were: teratozoospermia, cryptozoospermia and high DNA fragmentation index. Discussion: According to the present study and current knowledge, the use of testicular spermatozoa after failed intracytoplasmic sperm injection procedure in non‐azoospermic men could be proposed instead of sperm donation in case of high sperm DNA fragmentation index, cryptozoospermia and teratozoospermia. A good oocyte response to ovarian stimulation during the previous assisted reproductive technology attempt will increase the chance of success. Although the main limitation of the current study is its retrospective nature, the use of the propensity score matching to perform causal inference study increases its reliability. Conclusion: The present study supports that the use of testicular spermatozoa outside the classical indication of azoospermia is a good option when the indication is well established. However, before proposing a testicular biopsy, an improvement in sperm characteristics should be considered by treating the causes of sperm alteration. [ABSTRACT FROM AUTHOR]

Objective: Ovarian carcinoma (OvCa) is more common in the elderly, but also affects the adolescent and young adult (AYA) generation, which refers to those aged 15–39 years. Although the characteristics of OvCa may differ between AYAs and non‐AYAs, limited information is currently available on differences in prognostic factors. Therefore, we herein investigated prognostic factors for and the prognosis of OvCa in AYAs. We also examined the prognostic impact of fertility‐sparing surgery in a subgroup analysis. Methods: We retrospectively collected data on 4897 patients with OvCa from the databases of multiple institutions and ultimately included 1161 patients with epithelial ovarian cancer (EOC). We performed a survival analysis to compare AYAs and non‐AYAs with backgrounds that conformed to those of AYAs using the propensity score (PS) matching method. A Cox regression analysis was also conducted to evaluate each predictor of recurrence‐free survival (RFS) and overall survival (OS) in the original population. As a subgroup analysis, a multivariate analysis stratified by the AYA and non‐AYA generations was performed. Results: In total, 119 AYA patients were included in this study. After PS adjustments, no significant differences were observed in RFS or OS between AYAs and non‐AYAs. Prognostic factors differed between AYAs and non‐AYAs, particularly in histology and cytology. A multivariate analysis stratified by the AYA and non‐AYA generations described that uterine‐preserving surgery (UPS) did not have a significant impact on the prognosis of AYAs or non‐AYAs. In cases with recurrence, no significant differences were observed in RFS and recurrent sites in the two groups. Conclusion: Characteristic prognostic factors for EOC in AYAs were identified. The present results indicate the limited prognostic impact of UPS for EOC in AYAs. Synopsis: There were no significant differences in prognosis between AYAs and non‐AYAs. AYAs had specific prognostic factors and uterine‐preserving surgery was not a significant prognostic factor. [ABSTRACT FROM AUTHOR]

Pilot tends to have a high prevalence of dyslipidemia. The present study aimed to identify key factors of pilot hyperlipidemia through thorough analysis of physical examination data, and to provide pilot-targeted health guidance to manage hyperlipidemia risks. The physical examination data of 1,253 pilot inpatients from January 2019 to June 2022, were evaluated and divided into two groups based on whether or not the pilot had hyperlipidemia. A total of three multivariate analysis models including logistic model, multilevel model and boosting propensity score were applied to find the risk factors of pilot hyperlipidemia. In the group of pilots with hyperlipidemia, four risk factors, including thrombin time, carbohydrate antigen 199, lymphocyte count and rheumatoid factor, were significantly different from pilots without hyperlipidemia, which might be positively associated with the incidence of hyperlipidemia. In future studies regarding pilots, whether hyperlipidemia is connected to abnormalities in thrombin time, carbohydrate antigen 199 and rheumatoid factor should be further explored. Based on the findings of the present study, pilot health management should be more refined and personalized, and attention should be paid to the risk factors of hyperlipidemia including diet and lifestyle. [ABSTRACT FROM AUTHOR]

Background: The efficacy of adjuvant chemotherapy for high-risk stage II colon cancer (CC) has not been well established. Using propensity score matching, we previously reported that the 3-year disease-free survival (DFS) rate was significantly higher in patients treated with uracil and tegafur plus leucovorin (UFT/LV) against surgery alone. We report the final results, including updated 5-year overall survival (OS) rates and risk factor analysis outcomes. Methods: In total, 1902 high-risk stage II CC patients with T4, perforation/penetration, poorly differentiated adenocarcinoma/mucinous carcinoma, and/or < 12 dissected lymph nodes were enrolled in this prospective, non-randomized controlled study based on their self-selected treatment. Oral UFT/LV therapy was administered for six months after surgery. Results: Of the 1880 eligible patients, 402 in Group A (surgery alone) and 804 in Group B (UFT/LV) were propensity score-matched. The 5-year DFS rate was significantly higher in Group B than in Group A (P = 0.0008). The 5-year OS rates were not significantly different between groups. The inverse probability of treatment weighting revealed significantly higher 5-year DFS (P = 0.0006) and 5-year OS (P = 0.0122) rates in group B than in group A. Multivariate analyses revealed that male sex, age ≥ 70 years, T4, < 12 dissected lymph nodes, and no adjuvant chemotherapy were significant risk factors for DFS and/or OS. Conclusion: The follow-up data from our prospective non-randomized controlled study revealed a considerable survival advantage in DFS offered by adjuvant chemotherapy with UFT/LV administered for six months over surgery alone in individuals with high-risk stage II CC. Trial registration: Japan Registry of Clinical Trials: jRCTs031180155 (date of registration: 25/02/2019), UMIN Clinical Trials Registry: UMIN000007783 (date of registration: 18/04/2012). [ABSTRACT FROM AUTHOR]

Objectives: To compare the efficacy of transarterial chemoembolization (TACE) and radiofrequency ablation (RFA) for patients with single small (≤ 3 cm) hepatocellular carcinoma (HCC) and preserved liver function (Child-Pugh class A). Materials and methods: The clinical features of treatment-naïve patients who underwent TACE and RFA as first-line treatment were balanced through propensity score matching (PSM). The primary endpoint was overall survival (OS), and the secondary endpoints were local tumor recurrence (LTR) and recurrence-free survival (RFS). Results: The analysis included 440 patients who received TACE, and 430 patients who received RFA. After PSM adjustment (323 pairs), the 5- and 10-year OS rates were 81% and 61%, respectively, in patients who underwent RFA, and 77% and 51%, respectively, for patients who underwent TACE (p = 0.021). Subgroup analyses showed that OS, LTR, and RFS were homogeneously better in the RFA group. Conclusion: RFA was associated with better survival outcomes than TACE in patients with single small HCC and preserved liver function. Clinical relevance statement: This large-scale comparative study provides evidence that radiofrequency ablation has a better overall survival rate than chemoembolization for small (≤ 3 cm) hepatocellular carcinomas. Key Points: • The relative effectiveness of transarterial chemoembolization (TACE) and radiofrequency ablation (RFA) for early HCC is unclear. • Overall survival rate was significantly higher in the RFA group. • The effects of RFA on overall survival, local tumor recurrence, and recurrence-free survival were homogeneously better in all subgroups. [ABSTRACT FROM AUTHOR]

The randomized control trial (RCT) is the primary experimental design in education research due to its strong internal validity for causal inference. However, in situations where RCTs are not feasible or ethical, quasi-experiments are alternatives to establish causal inference. This paper serves as an introduction to several quasi-experimental designs: regression discontinuity design, difference-in-differences analysis, interrupted time series design, instrumental variable analysis, and propensity score analysis with examples in education research. [ABSTRACT FROM AUTHOR]

As evidence from evaluation and experimental studies continue to influence decision and policymaking, applied researchers and practitioners require tools to derive valid and credible inferences. Over the past several decades, research in causal inference has progressed with the development and application of propensity scores. Since their inception, propensity scores have made an important contribution to the improvement in estimation of causal impacts, particularly in the absence of randomization. When certain core assumptions hold, propensity score-based methods allow for bias-reduced estimation of average treatment effects. In addition to their important role in causal studies, propensity scores have also been integral in improving generalizations from causal studies, specifically when study samples are not randomly selected from a target population of inference. The current study provides an overview of propensity scores, a discussion of the assumptions needed to ensure their validity, and an illustration of the methods both for causal inference and generalization. We highlight the importance of propensity score methods and discuss current applications and directions for ongoing and future research. [ABSTRACT FROM AUTHOR]

In this paper, we advance the application of empirical likelihood (EL) for missing response problems. Inspired by remedies for the shortcomings of EL for parameter hypothesis testing, we modify the EL approach used for statistical inference on the mean response when the response is subject to missing behavior. We propose consistent mean estimators, and associated confidence intervals. We extend the approach to estimate the average treatment effect in causal inference settings. We detail the analogous estimators for average treatment effect, prove their consistency, and example their use in estimating the average effect of smoking on renal function of the patients with atherosclerotic renal-artery stenosis and elevated blood pressure, chronic kidney disease, or both. Our proposed estimators outperform the historical mean estimators under missing responses and causal inference settings in terms of simulated relative RMSE and coverage probability on average. [ABSTRACT FROM AUTHOR]

Abstract Background Previous research has shown that the use of renin-angiotensin system (RAS) blockers is linked to a lower prevalence of posttraumatic stress disorder (PTSD), but longitudinal studies are scarce. We aimed to estimate the association between the use of RAS blockers and the risk of PTSD among individuals taking antihypertensive medications. Methods This longitudinal study included participants aged 40–69 from the UK Biobank. Exposure data were obtained from the initial assessment (2006–10), while outcome data were obtained from the online mental health questionnaire administered 6–11 years later (2016–17). We included participants who were under antihypertensive treatment and did not have a prior diagnosis of PTSD before the initial assessment. Use of RAS blockers was defined as self-reported regular use, at the initial assessment, of angiotensin-converting enzyme inhibitor (ACEi) or angiotensin receptor blocker (ARB). Among participants who experienced adverse life experiences, cases of probable PTSD were defined with the six-item PTSD Checklist-Civilian version score ≥ 14. Logistic regression with inverse probability of treatment weighting was used to estimate the odds ratios (ORs) and 95% confidence interval (CI) for the association between RAS blocker use and the risk of probable PTSD. Results Of the 15,954 participants (mean age = 59.9 years; 42.6% women) under antihypertensive treatment with no prior history of PTSD at the initial assessment, 64.5% were taking RAS blockers. After a mean follow-up of 7.5 years, 1,249 (7.8%) were newly identified with probable PTSD. RAS blocker users had a lower risk of probable PTSD than RAS blocker non-users (OR = 0.84 [95% CI: 0.75–0.94]), whereas the use of other antihypertensive medications showed no such association (users vs. non-users; calcium channel blockers, OR = 0.99 [95% CI: 0.88–1.11]; beta-blockers, 1.20 [1.08–1.34]; and thiazide-related diuretics, 1.15 [1.03–1.29]). The association between probable PTSD risk and the use of ACEi vs. ARB showed no significant difference (p = 0.96). Conclusions Among individuals under antihypertensive treatment, the use of RAS blockers was associated with a decreased risk of probable PTSD. This added benefit of RAS blockers should be considered in the selection of antihypertensive medications.

Abstract Background Propensity scores (PS) are typically evaluated using balance metrics that focus on covariate balance, often without considering their predictive power for the outcome. This approach may not always result in optimal bias reduction in the treatment effect estimate. To address this issue, evaluating covariate balance through prognostic scores, which account for the relationship between covariates and the outcome, has been proposed. Similarly, using a typical model averaging approach for PS estimation that minimizes prediction error for treatment status and covariate imbalance does not necessarily optimize PS-based confounding adjustment. As an alternative approach, using the averaged PS model that minimizes inter-group differences in the prognostic score may further reduce bias in the treatment effect estimate. Moreover, since the prognostic score is also an estimated quantity, model averaging in the prognostic scores can help identify a better prognostic score model. Utilizing the model-averaged prognostic scores as the balance metric for constructing the averaged PS model can contribute to further decreasing bias in treatment effect estimates. This paper demonstrates the effectiveness of the PS model averaging approach based on prognostic score balance and proposes a method that uses the model-averaged prognostic score as a balance metric, evaluating its performance through simulations and empirical analysis. Methods We conduct a series of simulations alongside an analysis of empirical observational data to compare the performances of weighted treatment effect estimates using the proposed and existing approaches. In our examination, we separately provid four candidate estimates for the PS and prognostic score models using traditional regression and machine learning methods. The model averaging of PS based on these candidate estimators is performed to either maximize the prediction accuracy of the treatment or to minimize intergroup differences in covariate distributions or prognostic scores. We also utilize not only the prognostic scores from each candidate model but also an averaged score that best predicted the outcome, for the balance assessment. Results The simulation and empirical data analysis reveal that our proposed model-averaging approaches for PS estimation consistently yield lower bias and less variability in treatment effect estimates across various scenarios compared to existing methods. Specifically, using the optimally averaged prognostic scores as a balance metric significantly improves the robustness of the weighted treatment effect estimates. Discussion The prognostic score-based model averaging approach for estimating PS can outperform existing model averaging methods. In particular, the estimator using the model averaging prognostic score as a balance metric can produce more robust estimates. Since our results are obtained under relatively simple conditions, applying them to real data analysis requires adjustments to obtain accurate estimates according to the complexity and dimensionality of the data. Conclusions Using the prognostic score as the balance metric for the PS model averaging enhances the performance of the treatment effect estimator, which can be recommended for a wide variety of situations. When applying the proposed method to real-world data, it is important to use it in conjunction with techniques that mitigate issues arising from the complexity and high dimensionality of the data.

Doyoung Kwak,1 Yuanjie Liang,2 Xu Shi,3 Xi Tan2 1Department of Electrical & Computer Engineering, Texas A&M University, College Station, TX, USA; 2Novo Nordisk Inc, Plainsboro, NJ, USA; 3Department of Biostatistics, University of Michigan, Ann Arbor, MI, USACorrespondence: Xi Tan, Email mxtz@novonordisk.comPurpose: Observational research provides valuable insights into treatments used in patient populations in real-world settings. However, confounding is likely to occur if there are differences in patient characteristics associated with both the exposure and outcome between the groups being evaluated. One approach to reduce confounding and facilitate unbiased comparisons is inverse probability of treatment weighting (IPTW) using propensity scores. Machine learning (ML) and entropy balancing can potentially be used in generating propensity scores for IPTW, but there is limited literature on this application. We aimed to assess the feasibility of applying these methods for reducing confounding in observational studies. These methods were assessed in a study comparing cardiovascular outcomes in adults with type 2 diabetes and established atherosclerotic cardiovascular disease taking once-weekly glucagon-like peptide-1 receptor agonists or dipeptidyl peptidase-4 inhibitors.Methods: We applied advanced methods to generate the propensity scores compared to the original logistic regression method in terms of covariate balance. After calculating weights, a weighted Cox proportional hazards model was used to calculate the sample average treatment effect. Support Vector Classification, Support Vector Regression, XGBoost, and LightGBM were the ML models used. Entropy balancing was also performed on features identified in the original cardiovascular outcomes study.Results: Accuracy (range: 0.71 to 0.73), area under the curve (0.77 to 0.79), precision (0.53 to 0.60), recall (0.66 to 0.68), and F1 score (0.60 to 0.64) were similar between all of the advanced propensity score methods and traditional logistic regression. Among ML models, only XGBoost achieved balance in all measured baseline characteristics between the two treatment groups, closely approximating the performance of the original logistic regression. Entropy balancing weights provided the best performance among all models in balancing baseline characteristics, achieving near perfect balancing.Conclusion: Among the advanced methods examined, entropy balancing weights performed the best for optimizing balancing and can produce similar results compared to traditional logistic regression.Keywords: propensity score, machine learning, entropy balancing, type 2 diabetes, glucagon-like peptide-1 receptor agonists

Abstract Parkinson’s disease (PD) reports high rates of morbidity and mortality, but the risk of adverse cardiovascular outcomes in patients with PD has not been fully elucidated. This bi-center retrospective cohort study using the electronic health records (EHR) database of two tertiary hospitals screened a total of 327,292 subjects who visited the outpatient clinic, and 1194 patients with PD were propensity score-matched with a control population. The primary outcome was the occurrence of major adverse cardiovascular events (MACE). Key secondary outcomes included all-cause death, cardiovascular (CV) death, stroke, myocardial infarction (MI), heart failure hospitalization and 30-day CV death. After PS matching, MACE occurrence was not significantly different between PD and non-PD groups (18.2% vs. 17.5%, log-rank p = 0.98). Key secondary outcomes were also similar between the two groups. In patients with PD, MACE rate, and also CV risk score, were higher in patients with more severe PD (according to Hoehn and Yahr scale and unified Parkinson’s disease rating scale), and after multivariable analysis, PD severity was not an independent predictor of MACE. Patients with PD are at an increased risk of adverse cardiovascular outcomes, but the contribution from other common CV risk factors cannot be ignored. The management of prevalent CV risk factors is therefore important in mitigating adverse outcomes among patients with PD.

Abstract Background This study evaluated the real-world impact of acupuncture on analgesics and healthcare resource utilization among breast cancer survivors. Methods From a United States (US) commercial claims database (25% random sample of IQVIA PharMetrics® Plus for Academics), we selected 18–63 years old malignant breast cancer survivors experiencing pain and ≥ 1 year removed from cancer diagnosis. Using the difference-in-difference technique, annualized changes in analgesics [prevalence, rates of short-term (

Background: Attention-deficit/hyperactivity disorder (ADHD) is associated with increased costs for the family, the health care system and the society. Previous cost-of-illness studies in Germany usually focused on prevalent ADHD. This study addressed the research gap on health care resource utilisation and costs of children and adolescents with incident ADHD diagnosis using nationwide claims data from the statutory health insurance DAK-Gesundheit. Methods: A matched-control design (propensity score matching, 1:3 ratio) was used to examine the health care costs of incident ADHD patients compared with a non-ADHD control group, considering an observation period of four quarters. Besides bivariate statistics, multivariate analyses of total costs were used to consider relevant covariates. Results: Total health care costs for children and adolescents with ADHD in the first year after diagnosis exceeded those of the control group by € 1,505.3. According to the multivariate analysis, the group with incident ADHD had significantly higher (2.86-fold) health care costs when compared with non-ADHD peers. Sensitivity analyses proved these findings. In addition, the analyses identified children’s age and comorbidity index to be significantly associated with increased costs. Conclusions: ADHD in children and adolescents is associated with a significant economic burden. The results emphasise the need for social awareness, prevention, appropriate treatment and research efforts.

Background: The aim of this study was to determine whether inflammatory bowel disease (IBD) is associated with the risk of developing prostate cancer (PCa) through a population-based study. Materials and methods: Male patients aged ≥40 years, diagnosed with IBD from 2010 to 2013 and without IBD were identified and followed-up till 2019. A matched cohort of male patients with and without IBD in a ratio of 1:4 was created based on age, income level, and Charlson comorbidity index. Multivariate Cox regression analysis was conducted to evaluate the association of IBD with the prescence of PCa and PCa requiring definitive treatment within 1 year of diagnosis. The hazard ratio (HR) and 95% confidence interval (CI) were stratified by Crohn's disease, ulcerative colitis (UC), and subtypes. Results: After matching, 15,751 IBD patients and 62,346 controls were analyzed. Over a median follow-up period of 96 months, the HR for PCa was significantly increased in patients with IBD (HR: 2.44; 95% CI: 2.08–2.86, P

Abstract In settings requiring synthetic data generation based on a clinical cohort, e.g., due to data protection regulations, heterogeneity across individuals might be a nuisance that we need to control or faithfully preserve. The sources of such heterogeneity might be known, e.g., as indicated by sub-groups labels, or might be unknown and thus reflected only in properties of distributions, such as bimodality or skewness. We investigate how such heterogeneity can be preserved and controlled when obtaining synthetic data from variational autoencoders (VAEs), i.e., a generative deep learning technique that utilizes a low-dimensional latent representation. To faithfully reproduce unknown heterogeneity reflected in marginal distributions, we propose to combine VAEs with pre-transformations. For dealing with known heterogeneity due to sub-groups, we complement VAEs with models for group membership, specifically from propensity score regression. The evaluation is performed with a realistic simulation design that features sub-groups and challenging marginal distributions. The proposed approach faithfully recovers the latter, compared to synthetic data approaches that focus purely on marginal distributions. Propensity scores add complementary information, e.g., when visualized in the latent space, and enable sampling of synthetic data with or without sub-group specific characteristics. We also illustrate the proposed approach with real data from an international stroke trial that exhibits considerable distribution differences between study sites, in addition to bimodality. These results indicate that describing heterogeneity by statistical approaches, such as propensity score regression, might be more generally useful for complementing generative deep learning for obtaining synthetic data that faithfully reflects structure from clinical cohorts.

In biomedical science, analyzing treatment effect heterogeneity plays an essential role in assisting personalized medicine. The main goals of analyzing treatment effect heterogeneity include estimating treatment effects in clinically relevant subgroups and predicting whether a patient subpopulation might benefit from a particular treatment. Conventional approaches often evaluate the subgroup treatment effects via parametric modeling and can thus be susceptible to model mis-specifications. In this paper, we take a model-free semiparametric perspective and aim to efficiently evaluate the heterogeneous treatment effects of multiple subgroups simultaneously under the one-step targeted maximum-likelihood estimation (TMLE) framework. When the number of subgroups is large, we further expand this path of research by looking at a variation of the one-step TMLE that is robust to the presence of small estimated propensity scores in finite samples. From our simulations, our method demonstrates substantial finite sample improvements compared to conventional methods. In a case study, our method unveils the potential treatment effect heterogeneity of rs12916-T allele (a proxy for statin usage) in decreasing Alzheimers disease risk.