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Background: Paraesophageal hernia repairs (PEHRs) have high rates of radiographic recurrence, with some patients requiring repeat operation. This study characterizes patients who underwent PEHR to identify the factors associated with postoperative symptom improvement and radiographic recurrence. We furthermore use propensity score matching to compare patients undergoing initial and reoperative PEHR to identify the factors predictive of recurrence or need for reoperation. Methods: After IRB approval, patients who underwent PEHR at a tertiary care center between January 2018 and December 2022 were identified. Patient characteristics, preoperative imaging, operative findings, and postoperative outcomes were recorded. A computational generalization of inverse propensity score weight was then used to construct populations of initial and redo PEHR patients with similar covariate distributions. Results: A total of 244 patients underwent PEHR (78.7% female, mean age 65.4 ± 12.3 years). Most repairs were performed with crural closure (81.4%) and fundoplication (71.7%) with 14.2% utilizing mesh. Postoperatively, 76.5% of patients had subjective symptom improvement and of 157 patients with postoperative imaging, 52.9% had evidence of radiographic recurrence at a mean follow-up of 10.4 ± 13.6 months. Only 4.9% of patients required a redo operation. Hernia type, crural closure, fundoplication, and mesh usage were not predictors of radiographic recurrence or symptom improvement (P > 0.05). Propensity weight score analysis of 50 redo PEHRs compared to a matched cohort of 194 initial operations revealed lower rates of postoperative symptom improvement (P < 0.05) but no differences in need for revision, complication rates, ED visits, or readmissions. Conclusions: Most PEHR patients have symptomatic improvement with minimal complications and reoperations despite frequent radiographic recurrence. Hernia type, crural closure, fundoplication, and mesh usage were not significantly associated with recurrence or symptom improvement. Compared to initial PEHR, reoperative PEHRs had lower rates of symptom improvement but similar rates of recurrence, complications, and need for reoperation. [ABSTRACT FROM AUTHOR]

In a clustered observational study, a treatment is assigned to groups and all units within the group are exposed to the treatment. We develop a new method for statistical adjustment in clustered observational studies using approximate balancing weights, a generalization of inverse propensity score weights that solve a convex optimization problem to find a set of weights that directly minimize a measure of covariate imbalance, subject to an additional penalty on the variance of the weights. We tailor the approximate balancing weights optimization problem to the clustered observational study setting by deriving an upper bound on the mean square error and finding weights that minimize this upper bound, linking the level of covariate balance to a bound on the bias. We implement the procedure by specializing the bound to a random cluster‐level effects model, leading to a variance penalty that incorporates the signal‐to‐noise ratio and penalizes the weight on individuals and the total weight on groups differently according to the the intra‐class correlation. [ABSTRACT FROM AUTHOR]

In education settings, treatments are often non-randomly assigned to clusters, such as schools or classrooms, while outcomes are measured for students. This research design is called the clustered observational study (COS). We examine the consequences of common support violations in the COS context. Common support violations occur when the covariate distributions of treated and control units have substantial areas of non-overlap. Such violations are likely to occur in a COS, especially with a small number of treated clusters. We provide a comprehensive review of methods for overlap violations in the context of COS designs. We provide an overview of diagnostic tests and trimming methods to ensure overlap holds for the distributions of treated and control covariates. We then outline how trimming changes the estimand and how profiling can be used to understand the causal quantity for which overlap holds. Finally, we demonstrate how steps to achieve adequate overlap can result in very narrowly defined causal effects that may have little policy relevance. We use data on Catholic schools to illustrate concepts throughout. [ABSTRACT FROM AUTHOR]

We present a practical guide for the analysis of regression discontinuity (RD) designs in biomedical contexts. We begin by introducing key concepts, assumptions, and estimands within both the continuity‐based framework and the local randomization framework. We then discuss modern estimation and inference methods within both frameworks, including approaches for bandwidth or local neighborhood selection, optimal treatment effect point estimation, and robust bias‐corrected inference methods for uncertainty quantification. We also overview empirical falsification tests that can be used to support key assumptions. Our discussion focuses on two particular features that are relevant in biomedical research: (i) fuzzy RD designs, which often arise when therapeutic treatments are based on clinical guidelines, but patients with scores near the cutoff are treated contrary to the assignment rule; and (ii) RD designs with discrete scores, which are ubiquitous in biomedical applications. We illustrate our discussion with three empirical applications: the effect CD4 guidelines for anti‐retroviral therapy on retention of HIV patients in South Africa, the effect of genetic guidelines for chemotherapy on breast cancer recurrence in the United States, and the effects of age‐based patient cost‐sharing on healthcare utilization in Taiwan. Complete replication materials employing publicly available data and statistical software in Python, R and Stata are provided, offering researchers all necessary tools to conduct an RD analysis. [ABSTRACT FROM AUTHOR]

Background: Regional lymph node micrometastases from Merkel cell carcinoma (MCC) can be treated with completion lymph node dissection (CLND) and/or radiation therapy (RT). It is unclear how these options compare in terms of survival benefits for patients. Patients and Methods: This retrospective cohort study used data from years 2012–2019 of the National Cancer Database. Patients with MCC and clinically negative, but pathologically positive, lymph node metastases who received RT to and/or CLND of the regional lymph node basin were included. Inverse probability weight balancing was performed using covariates followed by Cox proportional hazards modeling for survival analysis. Results: A total of 962 patients were included [median (interquartile range) age, 74 (67–80) years, 662 (68.8%) male patients, 926 (96.3%) white patients]. The majority (63%, n = 606) had a CLND only, while 18% (n = 173) had RT only, and 19% (n = 183) had both CLND and RT. From 2016 to 2019, usage of RT only increased from 10% to 31.8%. Multivariate analysis demonstrated that treatment modality was not associated with survival [RT versus CLND, hazard ratio (HR) 0.842, 95% confidence interval (CI) 0.621–1.142, p = 0.269, RT+CLND versus CLND, HR 1.029, 95% CI 0.775–1.367, p = 0.844]. This persisted after balancing weights (RT versus CLND, HR 0.837, 95% CI 0.614–1.142, p = 0.262, RT+CLND versus CLND, HR 1.085, 95% CI 0.801–1.470, p = 0.599). Conclusions: The usage of RT for nodal micrometastasis in MCC is increasing as compared with CLND. This strategy appears to be safe, with no significant difference in survival outcomes. [ABSTRACT FROM AUTHOR]

Background: Electronic health records (EHRs) offer opportunities for comparative effectiveness research to inform decision making. However, to provide useful evidence, these studies must address confounding and treatment effect heterogeneity according to unmeasured prognostic factors. Local instrumental variable (LIV) methods can help studies address these challenges, but have yet to be applied to EHR data. This article critically examines a LIV approach to evaluate the cost-effectiveness of emergency surgery (ES) for common acute conditions from EHRs. Methods: This article uses hospital episodes statistics (HES) data for emergency hospital admissions with acute appendicitis, diverticular disease, and abdominal wall hernia to 175 acute hospitals in England from 2010 to 2019. For each emergency admission, the instrumental variable for ES receipt was each hospital's ES rate in the year preceding the emergency admission. The LIV approach provided individual-level estimates of the incremental quality-adjusted life-years, costs and net monetary benefit of ES, which were aggregated to the overall population and subpopulations of interest, and contrasted with those from traditional IV and risk-adjustment approaches. Results: The study included 268,144 (appendicitis), 138,869 (diverticular disease), and 106,432 (hernia) patients. The instrument was found to be strong and to minimize covariate imbalance. For diverticular disease, the results differed by method; although the traditional approaches reported that, overall, ES was not cost-effective, the LIV approach reported that ES was cost-effective but with wide statistical uncertainty. For all 3 conditions, the LIV approach found heterogeneity in the cost-effectiveness estimates across population subgroups: in particular, ES was not cost-effective for patients with severe levels of frailty. Conclusions: EHRs can be combined with LIV methods to provide evidence on the cost-effectiveness of routinely provided interventions, while fully recognizing heterogeneity. Highlights: This article addresses the confounding and heterogeneity that arise when assessing the comparative effectiveness from electronic health records (EHR) data, by applying a local instrumental variable (LIV) approach to evaluate the cost-effectiveness of emergency surgery (ES) versus alternative strategies, for patients with common acute conditions (appendicitis, diverticular disease, and abdominal wall hernia). The instrumental variable, the hospital's tendency to operate, was found to be strongly associated with ES receipt and to minimize imbalances in baseline characteristics between the comparison groups. The LIV approach found that, for each condition, there was heterogeneity in the estimates of cost-effectiveness according to baseline characteristics. The study illustrates how an LIV approach can be applied to EHR data to provide cost-effectiveness estimates that recognize heterogeneity and can be used to inform decision making as well as to generate hypotheses for further research. [ABSTRACT FROM AUTHOR]

Background: Merkel cell carcinoma (MCC) is a rare cutaneous malignancy for which factors predictive of disease-specific survival (DSS) are poorly defined. Methods: Patients from six centers (2005–2020) with clinical stage I–II MCC who underwent sentinel lymph node (SLN) biopsy were included. Factors associated with DSS were identified using competing-risks regression analysis. Risk-score modeling was established using competing-risks regression on a training dataset and internally validated by point assignment to variables. Results: Of 604 patients, 474 (78.5%) and 128 (21.2%) patients had clinical stage I and II disease, respectively, and 189 (31.3%) had SLN metastases. The 5-year DSS rate was 81.8% with a median follow-up of 31 months. Prognostic factors associated with worse DSS included increasing age (hazard ratio [HR] 1.03, p = 0.046), male sex (HR 3.21, p = 0.021), immune compromise (HR 2.46, p = 0.013), presence of microsatellites (HR 2.65, p = 0.041), and regional nodal involvement (1 node: HR 2.48, p = 0.039; ≥2 nodes: HR 2.95, p = 0.026). An internally validated, risk-score model incorporating all of these factors was developed with good performance (AUC 0.738). Patients with ≤ 4.00 and > 4.00 points had 5-year DSS rates of 89.4% and 67.2%, respectively. Five-year DSS for pathologic stage I/II patients with > 4.00 points (n = 49) was 79.8% and for pathologic stage III patients with ≤ 4.00 points (n = 62) was 90.3%. Conclusions: A risk-score model, including patient and tumor factors, based on DSS improves prognostic assessment of patients with clinically localized MCC. This may inform surveillance strategies and patient selection for adjuvant therapy trials. [ABSTRACT FROM AUTHOR]

In some cluster randomized trials, subjects may not comply with their assigned treatment status. Such treatment noncompliance can create opportunities for spillover effects within clusters. Little research has focused on what can be learned in such context. This article provides a non-technical review of recent work on the complications that arise in cluster randomized trials where some units within treated clusters do not comply with treatment but the treatment spillovers over to these units. We motivate concepts using a hypothetical vaccine cluster randomized trial. We review that standard instrumental variable methods cannot recover the complier average causal effect in the presence of these spillovers. In fact, we review that without additional assumptions, little can be learned about compliance effects or spillover effects. We discuss one additional assumption that allows for bounds on a key causal effect. We also outline an estimator for these bounds. [ABSTRACT FROM AUTHOR]

Background: Results of an earlier retrospective study from our institution suggested that patients with triple negative breast cancer (TNBC) who had preoperative MRI may have had an improved local recurrence rate (LRR) after breast conserving surgery (BCS). We aimed to clarify the impact of preoperative MRI on surgical outcomes in an expanded TNBC cohort treated by BCS in a contemporary era. Methods: Our study cohort comprised 648 patients with TNBC who underwent BCS between 2009 and 2018. Demographic and clinical characteristics were compared between those with (n = 292, 45.1%) and without (n = 356, 54.9%) preoperative MRI. Multivariable logistic regression was performed to assess the association of preoperative MRI with surgical outcomes. Results: The crude LRR of 3.5% was lower than previously reported. Univariable analyses demonstrated that the LRR and re-excision rates in the MRI and no-MRI groups were 3.4 and 3.7%, 21.6% and 27.2%, p = 0.876 and p = 0.10, respectively. Multivariable logistic regression analyses demonstrated that preoperative MRI was not associated with a lower LRR: odds ratio (OR) = 1.42 (p = 0.5). During our study period, new margin guidelines and shave margins practice were adopted in 2014 and 2015. To account for their effects, the year of diagnosis/surgery and other clinical variables were adjusted in multivariable logistic regression and inverse probability weighting models to demonstrate that preoperative MRI remained associated with a lower re-excision risk, OR 0.56, p = 0.04l; and a lower re-excision rate, 23.15% versus 36.0%, p < 0.01, respectively. Conclusions: Our findings suggested that patients with TNBC anticipating BCS may benefit from preoperative MRI. [ABSTRACT FROM AUTHOR]

Background: Adequate lymphadenectomy with at least 16 nodes retrieved at the time of gastrectomy is a quality measure recommended to ensure adequate staging. The minimum nodal retrieval recommended after receipt of neoadjuvant chemotherapy (NACT) is less defined. Methods: Patients with clinical stages 1 to 3 gastric adenocarcinoma who received NACT and surgical resection were identified from the 2004–2015 National Cancer Database. The optimal nodal harvest number was calculated with Cox spline regression modeling. Cohorts with a nodal harvest higher or lower than this number were 1:1 propensity score-matched. Overall survival (OS) was analyzed using Kaplan-Meier survival estimates. Results: Among 4337 patients receiving NACT, the optimal minimal nodal harvest at gastrectomy was 23 nodes. Compared with the patients who had fewer than 23 nodes retrieved, the patients with at least 23 nodes examined (n = 1073, 24.7%) were more likely to be female (26.1% vs 22%; p = 0.006) and non-white (29.3% vs 18.5%; p < 0.0001), to have a Charlson-Deyo score of 0 (71.5% vs 66.8%; p = 0.005), and to have undergone resection at an academic facility (67.9% vs 51.5%; p < 0.0001). The patients with at least 23 nodes examined had higher proportions of high-grade tumor (62% vs 57.4%; p = 0.030), pT3 or pT4 tumor (56.3% vs 48.7%; p < 0.0001), body tumor (21.3% vs 12.5%; p < 0.0001), or antrum/pylorus tumor (15.3% vs 11.4%; p < 0.0001). The patients with at least 23 nodes were more likely to have lymph node metastases identified (61% vs 51%; p < 0.0001). After matching, the patients with at least 23 nodes (n = 990) demonstrated an improved 5-year OS (57.9% vs 49%; p = 0.001). Conclusions: The extent of lymphadenectomy during gastrectomy for gastric adenocarcinoma should not be reduced after NACT because adequate lymph node retrieval remains important for prognostication. [ABSTRACT FROM AUTHOR]

Background: In many randomized controlled trials, a substantial proportion of patients do not comply with the treatment protocol to which they have been randomly assigned. Randomized controlled trials are required to report results according to the intention-to-treat estimand, but recent methodological guidance recognizes the importance of estimating other causal quantities. Methods: This article outlines an analytical framework for randomized controlled trials with non-compliance. We apply the ICH E9 (R1) addendum and combine it with the potential outcomes framework to define key estimands, outline the major assumptions for identification of each estimand, and highlight the assumptions that cannot be verified from the randomized controlled trial data. We contrast the assumptions and estimates in a re-analysis of the REFLUX trial. We report alternative estimates for the effectiveness of receipt of laparoscopic surgery versus medical management for patients with gastro-intestinal reflux disease. Results: The article finds that adjusted as-treated and per-protocol estimates were similar in magnitude to those based intention-to-treat methods. Instrumental variable estimates of the complier average causal effect were larger, with wider confidence intervals. Conclusion: We recommend that in randomized controlled trials with non-compliance, studies should outline which estimand is most relevant to the study context, evaluate key assumptions, and present estimates from a range of methods as a sensitivity analysis. [ABSTRACT FROM AUTHOR]

We investigate the efficacy of surgical versus nonsurgical management for two gastrointestinal conditions, colitis and diverticulitis, using observational data. We deploy an instrumental variable design with surgeons' tendencies to operate as an instrument. Assuming instrument validity, we find that nonsurgical alternatives can reduce both hospital length of stay and the risk of complications, with estimated effects larger for septic patients than for nonseptic patients. The validity of our instrument is plausible but not ironclad, necessitating a sensitivity analysis. Existing sensitivity analyses for IV designs assume effect homogeneity, unlikely to hold here because of patient-specific physiology. We develop a new sensitivity analysis that accommodates arbitrary effect heterogeneity and exploits components explainable by observed features. We find that the results for nonseptic patients prove more robust to hidden bias despite having smaller estimated effects. For nonseptic patients, two individuals with identical observed characteristics would have to differ in their odds of assignment to a high tendency to operate surgeon by a factor of 2.34 to overturn our finding of a benefit for nonsurgical management in reducing length of stay. For septic patients, this value is only 1.64. Simulations illustrate that this phenomenon may be explained by differences in within-group heterogeneity. for this article, including a standardized description of the materials available for reproducing the work, are available as an online supplement. [ABSTRACT FROM AUTHOR]

Abstract–In nonexperimental settings, the regression discontinuity (RD) design is one of the most credible identification strategies for program evaluation and causal inference. However, RD treatment effect estimands are necessarily local, making statistical methods for the extrapolation of these effects a key area for development. We introduce a new method for extrapolation of RD effects that relies on the presence of multiple cutoffs, and is therefore design-based. Our approach employs an easy-to-interpret identifying assumption that mimics the idea of "common trends" in difference-in-differences designs. We illustrate our methods with data on a subsidized loan program on post-education attendance in Colombia, and offer new evidence on program effects for students with test scores away from the cutoff that determined program eligibility. for this article are available online. [ABSTRACT FROM AUTHOR]

Introduction: National guidelines specify against immediate breast reconstruction (IBR) among inflammatory breast cancer (IBC) patients. However, limited data exist regarding this practice. We report practice patterns and oncologic outcomes among nonmetastatic IBC patients receiving trimodality therapy, with or without IBR. Methods: Using the National Cancer Database, we identified nonmetastatic IBC patients treated with trimodality therapy from 2004 to 2016. Primary outcome was overall survival (OS), assessed on unadjusted analysis using Kaplan–Meier estimates and on adjusted analysis using multivariable Cox proportional hazards and inverse probability weighting (IPW) models. OS analysis was also conducted with propensity score matched (PSM) cohorts. Secondary outcomes included IBR utilization rates, time to postmastectomy radiotherapy (PMRT), and surgical outcomes. Results: 6589 women were included, including 5954 (90.4%) non-reconstructed and 635 (9.6%) IBR. Among IBR recipients, 250 (39.4%) underwent autologous reconstruction, 171 (26.9%) underwent implant-based reconstruction, and 214 (33.7%) unspecified. IBR utilization increased from 6.3% to 10.1% from 2004 to 2016 at a 4% average annual growth rate (P < 0.001). Median follow-up was 43 and 45 months for IBR and non-reconstructed patients, respectively (P = 0.29). On Cox multivariable analysis, IBR was associated with improved OS (HR 0.63, 95% CI 0.44–0.90, P = 0.01), but this association was not significant on IPW analysis (P = 0.06). In PSM cohorts, this association remained significant (HR 0.60, 95% CI 0.40–0.92, P = 0.02). Margin status, time to PMRT, 30-day readmission, and 30-/90-day mortality did not differ between groups (all P > 0.05). Conclusion: Although not endorsed by national guidelines, IBR is increasing among IBC patients; however, more granular data are needed to determine oncologic safety. [ABSTRACT FROM AUTHOR]

Background: National medical/surgical organizations have recommended the use of neoadjuvant endocrine therapy (NET) to bridge surgery delay of weeks to months for patients with hormone receptor positive (HR+) breast cancer during the ongoing coronavirus disease 2019 (COVID-19) pandemic. The effects of NET of varying durations on pathologic response are unclear. Using the National Cancer Database (NCDB), we evaluated objective response to short (< 9 weeks), moderate (9–27 weeks), and long (> 27 weeks) duration of NET. Patients and Methods: The study cohort included female patients diagnosed with nonmetastatic invasive HR+ breast cancer, stratifying by those who received NET versus no NET between 2004 and 2016. Pathologic response was grouped into four categories (complete, downstaged, stable, upstaged) by comparing clinical and pathologic staging data. Objective response to NET included complete, downstaged, and stable pathologic response. Clinical characteristics were compared using χ2 and analysis of variance (ANOVA) tests. Multivariable logistic regression was used to determine factors associated with NET use and objective response according to NET duration. Results: A minority (1.2%) received NET in our cohort. Factors associated with NET use included older age, non-Black patients, more advanced clinical stage, higher comorbidity score, government insurance, and lobular histology. Objective response rate (ORR) was 56.7%, 52.1%, and 49.0% after short, moderate, and long NET duration, respectively. Conclusion: Short NET duration did not result in an inferior ORR. Future study to evaluate the interaction between surgery delay and NET use on clinical outcome will provide insights into the safety of NET to bridge potential surgery delay in patients with HR+ breast cancer. [ABSTRACT FROM AUTHOR]

Social scientists use the concept of interactions to study effect dependency. In the causal inference literature, interaction terms may be used in two distinct type of analysis. The first type of analysis focuses on causal interactions, where the analyst is interested in whether two treatments have differing effects when both are administered. The second type of analysis focuses on effect modification, where the analyst investigates whether the effect of a single treatment varies across levels of a baseline covariate. While both forms of interaction analysis are typically conducted using the same type of statistical model, the identification assumptions for these two types of analysis are very different. In this paper, we clarify the difference between these two types of interaction analysis. We demonstrate that this distinction is mostly ignored in the political science literature. We conclude with a review of several applications where we show that the form of the interaction is critical to proper interpretation of empirical results. [ABSTRACT FROM AUTHOR]

Many interventions in education occur in settings where treatments are applied to groups. For example, a reading intervention may be implemented for all students in some schools and withheld from students in other schools. When such treatments are nonrandomly allocated, outcomes across the treated and control groups may differ due to the treatment or due to baseline differences between groups. When this is the case, researchers can use statistical adjustment to make treated and control groups similar in terms of observed characteristics. Recent work in statistics has developed matching methods designed for contexts where treatments are clustered. This form of matching, known as multilevel matching, may be well suited to many education applications where treatments are assigned to schools. In this article, we provide an extensive evaluation of multilevel matching and compare it to multilevel regression modeling. We evaluate multilevel matching methods in two ways. First, we use these matching methods in a within-study comparison design and attempt to recover treatment effect estimates from three clustered randomized trials. Second, we conduct a simulation study. We find evidence that generally favors an analytic approach to statistical adjustment that combines multilevel matching with regression adjustment. We conclude with an empirical application. [ABSTRACT FROM AUTHOR]

Southern states have used a variety of methods to disenfranchise African American voters. Empirical data on the effectiveness of these measures is rare. We present a unique data source from Louisiana that allows us to empirically document voter registration rates from the end of Reconstruction to the present. Using basic time series data, we document how voter registration rates changed over time in response to state restrictions. We then conduct a second analysis, which focuses on Louisiana's use of the Understanding Clause to reduce voter registration among Blacks. We show that in parishes that used the Understanding Clause, Black registration rates dropped by nearly 30 percentage points, with little effect on white registration. The findings of this paper have important implications for understanding the potential for discrimination in the enforcement of modern, ostensibly nonracial, voter eligibility requirements, such as voter ID laws, which grant substantial discretion to local officials in determining voter eligibility. [ABSTRACT FROM AUTHOR]

Much evidence in comparative effectiveness research is based on observational studies. Researchers who conduct observational studies typically assume that there are no unobservable differences between the treatment groups under comparison. Treatment effectiveness is estimated after adjusting for observed differences between comparison groups. However, estimates of treatment effectiveness may be biased because of misspecification of the statistical model. That is, if the method of treatment effect estimation imposes unduly strong functional form assumptions, treatment effect estimates may be inaccurate, leading to inappropriate recommendations about treatment decisions. We compare the performance of a wide variety of treatment effect estimation methods for the average treatment effect. We do so within the context of the REFLUX study from the United Kingdom. In REFLUX, participants were enrolled in either an randomized controlled trial (RCT) or an observational study arm. In the RCT, patients were randomly assigned to either surgery or medical management. In the patient preference arm, participants selected to either have surgery or medical management. We attempt to recover the treatment effect estimate from the RCT using the data from the patient preference arms of the study. We vary the method of treatment effect estimation and record which methods are successful and which are not. We apply more than 20 different methods, including standard regression models as well as advanced machine learning methods. We find that simple propensity score matching methods provide the least accurate estimates versus the RCT benchmark. We find variation in performance across the other methods, with some, but not all recovering the experimental benchmark. We conclude that future studies should use multiple methods of estimation to fully represent uncertainty according to the choice of estimation approach. [ABSTRACT FROM AUTHOR]

Background: The COVID-19 pandemic has resulted in large-scale healthcare restrictions to control viral spread, reducing operating room censuses to include only medically necessary surgeries. The impact of restrictions on which patients undergo surgical procedures and their perioperative outcomes is less understood. Methods: Adult patients who underwent medically necessary surgical procedures at our institution during a restricted operative period due to the COVID-19 pandemic (March 23-April 24, 2020) were compared to patients undergoing procedures during a similar time period in the pre-COVID-19 era (March 25-April 26, 2019). Cardinal matching and differences in means were utilized to analyze perioperative outcomes. Results: 857 patients had surgery in 2019 (pre-COVID-19) and 212 patients had surgery in 2020 (COVID-19). The COVID-19 era cohort had a higher proportion of patients who were male (61.3% vs. 44.5%, P < 0.0001), were White (83.5% vs. 68.7%, P < 0.001), had private insurance (62.7% vs. 54.3%, p 0.05), were ASA classification 4 (10.9% vs. 3%, P < 0.0001), and underwent oncologic procedures (69.3% vs. 42.7%, P < 0.0001). Following 1:1 cardinal matching, COVID-19 era patients (N = 157) had a decreased likelihood of discharge to a nursing facility (risk difference-8.3, P < 0.0001) and shorter median length of stay (risk difference-0.6, p 0.04) compared to pre-COVID-19 era patients. There was no difference between the two patient cohorts in overall morbidity and 30-day readmission. Conclusions: COVID-19 restrictions on surgical operations were associated with a change in the racial and insurance demographics in patients undergoing medically necessary surgical procedures but were not associated with worse postoperative morbidity. Further study is necessary to better identify the causes for patient demographic differences. [ABSTRACT FROM AUTHOR]

Instrumental variable (IV) analyses are becoming common in health services research and epidemiology. Most IV analyses use naturally occurring instruments, such as distance to a hospital. In these analyses, investigators must assume that the instrument is as-if randomly assigned. This assumption cannot be tested directly, but it can be falsified. Most IV falsification tests compare relative prevalence or bias in observed covariates between the instrument and exposure. These tests require investigators to make covariate-by-covariate judgments about the validity of the IV design. Often, only some covariates are well-balanced, making it unclear whether as-if randomization can be assumed for the instrument. We propose an alternative falsification test that compares IV balance or bias with the balance or bias that would have been produced under randomization. A key advantage of our test is that it allows for global balance measures as well as easily interpretable graphical comparisons. Furthermore, our test does not rely on parametric assumptions and can be used to validly assess whether the instrument is significantly closer to being as-if randomized than the exposure. We demonstrate our approach using data from (SPOT)light, a prospective cohort study carried out in 48 National Health Service hospitals in the United Kingdom between November 1, 2010, and December 31, 2011. This study used bed availability in the intensive care unit as an instrument for admission to the intensive care unit. [ABSTRACT FROM AUTHOR]

Summary: Instrumental variable methods, subject to appropriate identification assumptions, enable consistent estimation of causal effects in the presence of unobserved confounding. Near–far matching has been proposed as one analytic method to improve inference by strengthening the effect of the instrument on the exposure and balancing observable characteristics between groups of subjects with low and high values of the instrument. However, in settings with hierarchical data (e.g. patients nested within hospitals), or where several covariate interactions must be balanced, conventional near–far matching algorithms may fail to achieve the requisite covariate balance. We develop a new matching algorithm, that combines near–far matching with refined covariate balance, to balance large numbers of nominal covariates while also strengthening the instrumental variable. This extension of near–far matching is motivated by a case‐study that aims to identify the causal effect of prompt admission to an intensive care unit on 7‐day and 28‐day mortality. [ABSTRACT FROM AUTHOR]

Background: Hyperbilirubinemia is commonly observed in patients requiring pancreatoduodenectomy (PD). Thus far, literature regarding the danger of operating in the setting of hyperbilirubinemia is equivocal. What remains undefined is at what specific level of bilirubin there is an adverse safety profile for undergoing PD. The aim of this study is to identify the optimal safety profile of patients with hyperbilirubinemia undergoing PD. Patients and Methods: The present work analyzed 803 PDs from 2004 to 2018. A generalized additive model was used to determine cutoff values of total serum bilirubin (TB) that were associated with increases in adverse outcomes, including 90-day mortality. Subgroup comparisons and biliary stent-specific analyses were performed for patients with TB below and above the cutoff. Results: TB of 13 mg/dL was associated with an increase in 90-day mortality (P = 0.043) and was the dominant risk factor on multivariate logistic regression [odds ratio (OR) 8.193, P = 0.001]. Increased TB levels were also associated with reoperations, number of complications per patient, and length of stay. Patients with TB greater than or equal to 13 mg/dL (TB ≥ 13) who received successful biliary decompression through stenting had less combined death and serious morbidity (P = 0.048). Conclusions: Preoperative TB ≥ 13 mg/dL was associated with increased 90-day mortality after PD. Reducing a TB ≥ 13 is generally recommended before proceeding to surgery. [ABSTRACT FROM AUTHOR]

Background: Prior to the advent of Oncotype DX 21-gene recurrence score (oDX) assay, the National Comprehensive Cancer Network (NCCN) guideline supported omission of adjuvant chemotherapy in patients with ≤ 1 cm (T1b) hormone receptor-positive (HR +), human epidermal growth factor receptor 2 (HER2−) node tumors. However, around 30% of these patients would have an oDX recurrence score that warrants consideration of adjuvant chemotherapy. To clarify the potential benefit of oDX in these patients, we performed a retrospective analysis comparing clinical outcomes of women with T1a or T1b, N0 HR + HER2− according to performance of oDX. Patients and Methods: After receiving institutional review board (IRB) approval, an institutional database was queried to identify patients with HR + HER2− ≤ T1bN0 tumors (n = 2307) diagnosed between 2009 and 2018. Patients were further stratified by recurrence score (RS) defined as low (< 18), intermediate (18–30), or high (> 30). Log-rank, Kaplan–Meier, and inverse probability of treatment weighting (IPW) analyses were used to compare disease-free survival (DFS) and overall survival (OS) across groups. Results: Performance of oDX (n = 1149, 49.8%) was associated with larger tumors, younger age, and White race. On univariate analysis, performance of oDX was associated with improved OS (P < 0.01). On multivariate IPW analysis, performance of oDX lengthened DFS by an average of 16.5 months, while OS was similar between groups (P < 0.01 and P = 0.73). The improved DFS was mainly driven by those with tumors ≥ T1b. Conclusions: Overall, outcomes were excellent regardless of oDX testing. Performance of oDX testing was associated with improved DFS in patients with tumors ≥ T1b. Our results support routine use of oDX testing in patients with tumors ≥ T1b. [ABSTRACT FROM AUTHOR]

Purpose: A recent study reported that time to adjuvant chemotherapy (TTC) > 30 days was significantly associated with worse OS and DFS in triple-negative breast cancer (TNBC). Earlier studies, however, found that worse outcomes were associated with TTC > 60 days or > 90 days. As the trend for mastectomy with reconstruction continues to rise, TTC of < 30 days is often not feasible due to wound-healing issues in some of these patients. To elucidate the impact of TTC, we sought to evaluate the clinical outcomes associated with TTC in a contemporary cohort treated for TNBC at a single institution. Methods: A single-institution database was queried to identify nonmetastatic TNBC patients who received adjuvant chemotherapy from 2009 to 2018. TTC was defined as interval between date of surgery and adjuvant chemotherapy start date. Median TTC was used to divide our cohort into four quartiles; ≤ 31, 32–42, 43–56, and > 56 days. Logrank, Kaplan–Meier, and inverse probability weighting (IPW) tests were used to analyze disease-free (DFS) and overall survival (OS). Results: The mean TTC of our study cohort (n = 724) was 48 days (median TTC = 42 days). Black race, mastectomy without adjuvant radiation, and mastectomy with immediate reconstruction were associated with delayed TTC (all p-values < 0.01). In multivariate IPW analysis, TTC > 56 (n = 173) days did not impact DFS or OS compared to TTC ≤ 31 (n = 198) days (p = 0.27 and p = 0.21, respectively). Similar results were seen during subgroup analysis for groups identified as higher risk for delayed TTC. Conclusion: Our results demonstrated that TTC was not significant or significantly associated with DFS or OS in patient receiving chemotherapy for operable TNBC. Our results were reassuring for patients electing mastectomy with immediate reconstruction, who may experience a longer TTC. [ABSTRACT FROM AUTHOR]

The article focuses on the use of instrumental variables (IVs) in outcomes research. Topics discussed the variables highlight a number of ways to improve the quality of research that based on IV design, mentions the discussion on IV methods that are consistent with the results from randomized trials, and information on the empirical evidence for the IV design.

Pretreatment selection or censoring ("selection on treatment") can occur when two treatment levels are compared ignoring the third option of neither treatment, in "censoring by death" settings where treatment is only defined for those who survive long enough to receive it, or in general in studies where the treatment is only defined for a subset of the population. Unfortunately, the standard instrumental variable (IV) estimand is not defined in the presence of such selection, so we consider estimating a new survivor-complier causal effect. Although this effect is generally not identified under standard IV assumptions, it is possible to construct sharp bounds. We derive these bounds and give a corresponding data-driven sensitivity analysis, along with nonparametric yet efficient estimation methods. Importantly, our approach allows for high-dimensional confounding adjustment, and valid inference even after employing machine learning. Incorporating covariates can tighten bounds dramatically, especially when they are strong predictors of the selection process. We apply the methods in a UK cohort study of critical care patients to examine the mortality effects of prompt admission to the intensive care unit, using ICU bed availability as an instrument. Supplementary materials for this article are available online. [ABSTRACT FROM AUTHOR]

Confounding by indication is a critical challenge in evaluating the effectiveness of surgical interventions using observational data. The threat from confounding is compounded when using medical claims data due to the inability to measure risk severity. If there are unobserved differences in risk severity across patients, treatment effect estimates based on methods such a multivariate regression may be biased in an unknown direction. A research design based on instrumental variables offers one possibility for reducing bias from unobserved confounding compared to risk adjustment with observed confounders. This study investigates whether a physician's preference for operative care is a valid instrumental variable for studying the effect of emergency surgery. We review the plausibility of the necessary causal assumptions in an investigation of the effect of emergency general surgery (EGS) on inpatient mortality among adults using medical claims data from Florida, Pennsylvania, and New York in 2012–2013. In a departure from the extant literature, we use the framework of stochastic monotonicity which is more plausible in the context of a preference-based instrument. We compare estimates from an instrumental variables design to estimates from a design based on matching that assumes all confounders are observed. Estimates from matching show lower mortality rates for patients that undergo EGS compared to estimates based in the instrumental variables framework. Results vary substantially by condition type. We also present sensitivity analyses as well as bounds for the population level average treatment effect. We conclude with a discussion of the interpretation of estimates from both approaches. [ABSTRACT FROM AUTHOR]