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It is well know that presidents with congressional majorities are better able to implement their legislative agendas. However, it is concluded that presidential attempts influence elections, and hence the findings indicative of presidential influence in previous congressional elections are discussed.

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.

While randomization inference is well developed for continuous and binary outcomes, there has been comparatively little work for outcomes with nonnegative support and clumping at zero. Typically, outcomes of this type have been modeled using parametric models that impose strong distributional assumptions. This article proposes new randomization inference procedures for nonnegative outcomes with clumping at zero. Instead of making distributional assumptions, we propose various assumptions about the nature of the response to treatment and use permutation inference for both testing and estimation. This approach allows for some natural goodness-of-fit tests for model assessment, as well as flexibility in selecting test statistics sensitive to different potential alternatives. We illustrate our approach using two randomized trials, where job training interventions were designed to increase earnings of participants. [ABSTRACT FROM AUTHOR]

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]

First, we would like to congratulate the authors for successfully hosting the causal inference data competition (referred to as Competition henceforth) and contributing a unique and thought-provoking article to the literature. The authors have provided a comprehensive and timely platform to evaluate the ever-growing number of methods used for covariate adjustment in observational studies. In our comment, we don't generally question the results of the competition, but we do wish to emphasize several other key elements about the role statistics plays in causal inference and observational studies. [ABSTRACT FROM AUTHOR]

Summary: The method of instrumental variables provides a framework to study causal effects in both randomized experiments with non‐compliance and in observational studies where natural circumstances produce as if random nudges to accept treatment. Traditionally, inference for instrumental variables relied on asymptotic approximations of the distribution of the Wald estimator or two‐stage least squares, often with structural modelling assumptions and/or moment conditions. We utilize the randomization inference approach to instrumental variables inference. First, we outline the exact method, which uses the randomized assignment of treatment in experiments as a basis for inference but lacks a closed form solution and may be computationally infeasible in many applications. We then provide an alternative to the exact method, the almost exact method, which is computationally feasible but retains the advantages of the exact method. We also review asymptotic methods of inference, including those associated with two‐stage least squares, and analytically compare them with randomization inference methods. We also perform additional comparisons by using a set of simulations. We conclude with three different applications from the social sciences. [ABSTRACT FROM AUTHOR]

Lack of insurance has been independently associated with an increased risk of in-hospital mortality after abdominal aortic aneurysm repair, possibly due to worse control of comorbidities and delays in diagnosis and treatment. Medicaid expansion has improved insurance rates and access to care, potentially benefiting these patients. We sought to assess the association between Medicaid expansion and outcomes after abdominal aortic aneurysm repair. A retrospective analysis of Healthcare Cost and Utilization Project State Inpatient Databases data from 14 states between 2012 and 2018 was conducted. The sample was restricted to first-record abdominal aortic aneurysm repairs in adults under age 65 in states that expanded Medicaid on January 1, 2014 (Medicaid expansion group) or had not expanded before December 31, 2018 (non-expansion group). The Medicaid expansion and non-expansion groups were compared between pre-expansion (2012-2013) and post-expansion (2014-2018) time periods to assess baseline demographic and operative differences. We used difference-in-differences multivariable logistic regression adjusted for patient factors, open vs endovascular repair, and standard errors clustered by state. Our primary outcome was in-hospital mortality. Outcomes were stratified by insurance type. We examined 8995 patients undergoing abdominal aortic aneurysm repair, including 3789 (42.1%) in non-expansion states and 5206 (57.9%) in Medicaid expansion states. Rates of Medicaid insurance were unchanged in non-expansion states but increased in Medicaid expansion states post-expansion (non-expansion: 10.9% to 9.8%; P =.346; expansion: 9.7% to 19.7%; P <.001). One in 10 patients from both non-expansion and Medicaid expansion states presented with ruptured aneurysms, which did not change over time. Rates of open repair decreased in both non-expansion and Medicaid expansion states over time (non-expansion: 25.1% to 19.2%; P <.001; expansion: 25.2% to 18.4%; P <.001). On adjusted difference-in-differences analysis between expansion and non-expansion states pre-to post-expansion, Medicaid expansion was associated with a 1.02% absolute reduction in in-hospital mortality among all patients (95% confidence interval, −1.87% to −0.17%; P =.019). Additionally, among patients who were either on Medicaid or were uninsured (ie, the patients most likely to be impacted by Medicaid expansion), a larger 4.17% decrease in in-hospital mortality was observed (95% confidence interval, −6.47% to −1.87%; P <.001). In contrast, no significant difference-in-difference in mortality was observed for privately insured patients. Medicaid expansion was associated with decreased in-hospital mortality after abdominal aortic aneurysm repair among all patients and particularly among patients who were either on Medicaid or were uninsured. Our results provide support for improved access to care for patients undergoing abdominal aortic aneurysm repair through Medicaid expansion. [ABSTRACT FROM AUTHOR]

What effect does a candidate's race have on coracial voter turnout? Recent studies have found mixed results, largely because it is difficult to separate the effect of candidate race from other factors that drive voter turnout. We argue that viability is a key element in the theory of turnout among coracial voters that has been overlooked in the extant literature. We develop a broad-based concept of candidate viability that is dependent on both the candidate and the electoral environment. To test this hypothesis, we make use of the unique runoff structure of mayoral elections in the state of Louisiana between 1988 and 2011.We argue that runoff elections heighten viability in ways rarely seen in most elections. We find that while there is an effect of candidate race on black turnout in general elections, the effect is much more robust in runoff elections. [ABSTRACT FROM AUTHOR]

A distinctive feature of a clustered observational study is its multilevel or nested data structure arising from the assignment of treatment, in a nonrandom manner, to groups or clusters of units or individuals. Examples are ubiquitous in the health and social sciences including patients in hospitals, employees in firms, and students in schools. What is the optimal matching strategy in a clustered observational study? At first thought, one might start by matching clusters of individuals and then, within matched clusters, continue by matching individuals. But as we discuss in this article, the optimal strategy is the opposite: in typical applications, where the intracluster correlation is not one, it is best to first match individuals and, once all possible combinations of matched individuals are known, then match clusters. In this article, we use dynamic and integer programming to implement this strategy and extend optimal matching methods to hierarchical and multilevel settings. Among other matched designs, our strategy can approximate a paired clustered randomized study by finding the largest sample of matched pairs of treated and control individuals within matched pairs of treated and control clusters that is balanced according to specifications given by the investigator. This strategy directly balances covariates both at the cluster and individual levels and does not require estimating the propensity score, although the propensity score can be balanced as an additional covariate. We illustrate our results with a case study of the comparative effectiveness of public versus private voucher schools in Chile, a question of intense policy debate in the country at the present. [ABSTRACT FROM AUTHOR]

In randomized controlled trials with non-adherence, instrumental variable (IV) methods are frequently used to report the complier average causal effect. With binary outcomes, many of the available IV estimation methods impose distributional assumptions. We develop a randomization-inference-based method of IV estimation for binary outcomes. The method is non-parametric and is based on Fisher's exact test, and estimates can be easily calculated from a set of 2×2 or 2×2×2 tables. Although we retain the standard IV identification assumptions for confidence regions and point estimates, the IV estimand under randomization inference is sample specific and does not assume that the randomized controlled trials participants are a random sample from the target population. We illustrate the method with the 'IMPROVE' trial that compares emergency endovascular versus open surgical repair for patients with ruptured aortic aneurysms. [ABSTRACT FROM AUTHOR]