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IMPORTANCE More than 300 000 hip fractures occur each year in the United States. Recent practice guidelines have advocated greater use of regional anesthesia for hip fracture surgery. OBJECTIVE To test the association of regional (ie, spinal or epidural) anesthesia vs general anesthesia with 30-day mortality and hospital length of stay after hip fracture. DESIGN, SETTING, AND PATIENTS We conducted a matched retrospective cohort study involving patients 50 years or older who were undergoing surgery for hip fracture at general acute care hospitals in New York State between July 1, 2004, and December 31, 2011. Our main analysis was a near-far instrumental variable match that paired patients who lived at different distances from hospitals that specialized in regional or general anesthesia. Supplementary analyses included a within-hospital match that paired patients within the same hospital and an across-hospital match that paired patients at different hospitals. EXPOSURES Spinal or epidural anesthesia; general anesthesia. MAIN OUTCOMES AND MEASURES Thirty-day mortality and hospital length of stay. Because the distribution of length of stay had long tails, we characterized this outcome using the Huber M estimate with Huber weights, a robust estimator similar to a trimmed mean. RESULTS Of 56 729 patients, 15 904 (28%) received regional anesthesia and 40 825 (72%) received general anesthesia. Overall, 3032 patients (5.3%) died. The M estimate of the length of stay was 6.2 days (95% CI, 6.2 to 6.2). The near-far matched analysis showed no significant difference in 30-day mortality by anesthesia type among the 21 514 patients included in this match: 583 of 10 757 matched patients (5.4%) who lived near a regional anesthesia– specialized hospital died vs 629 of 10 757 matched patients (5.8%) who lived near a general anesthesia–specialized hospital (instrumental variable estimate of risk difference, −1.1%; 95% CI, −2.8 to 0.5; P = .20). Supplementary analyses of within and across hospital patient matches yielded mortality findings to be similar to the main analysis. In the near-far match, regional anesthesia was associated with a 0.6-day shorter length of stay than general anesthesia (95% CI, −0.8 to −0.4, P < .001). Supplementary analyses also showed regional anesthesia to be associated with shorter length of stay, although the observed association was smaller in magnitude than in the main analysis. CONCLUSIONS AND RELEVANCE Among adults in acute care hospitals in New York State undergoing hip repair, the use of regional anesthesia compared with general anesthesia was not associated with lower 30-day mortality but was associated with a modestly shorter length of stay. These findings do not support a mortality benefit for regional anesthesia in this setting.

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Weighting methods are widely used to adjust for covariates in observational studies, sample surveys, and regression settings. In this paper, we study a class of recently proposed weighting methods which find the weights of minimum dispersion that approximately balance the covariates. We call these weights minimal weights and study them under a common optimization framework. The key observation is the connection between approximate covariate balance and shrinkage estimation of the propensity score. This connection leads to both theoretical and practical developments. From a theoretical standpoint, we characterize the asymptotic properties of minimal weights and show that, under standard smoothness conditions on the propensity score function, minimal weights are consistent estimates of the true inverse probability weights. Also, we show that the resulting weighting estimator is consistent, asymptotically normal, and semiparametrically efficient. From a practical standpoint, we present a finite sample oracle inequality that bounds the loss incurred by balancing more functions of the covariates than strictly needed. This inequality shows that minimal weights implicitly bound the number of active covariate balance constraints. We finally provide a tuning algorithm for choosing the degree of approximate balance in minimal weights. We conclude the paper with 1 arXiv:1705.00998v3 [stat.ME] 25 Apr 2019 four empirical studies that suggest approximate balance is preferable to exact balance, especially when there is limited overlap in covariate distributions. In these studies, we show that the root mean squared error of the weighting estimator can be reduced by as much as a half with approximate balance.

Suicide is a leading cause of death. A substantial proportion of the people who die by suicide come into contact with the health care system in the year before their death. This observation has resulted in the development of numerous suicide prediction tools to help target patients for preventive interventions. However, low sensitivity and low positive predictive value have led critics to argue that these tools have no clinical value. We review these tools and critiques here. We conclude that existing tools are suboptimal and that improvements, if they can be made, will require developers to work with more comprehensive predictor sets, staged screening designs, and advanced statistical analysis methods. We also conclude that although existing suicide prediction tools currently have little clinical value, and in some cases might do more harm than good, an even-handed assessment of the potential value of refined tools of this sort cannot currently be made because such an assessment would depend on evidence that currently does not exist about the effectiveness of preventive interventions. We argue that the only way to resolve this uncertainty

are for-profit enterprises not different in concept than a restaurant or retail store. Whether schools should be allowed to profit is an intensely controversial issue in Chile. On the one hand, supporters of for-profit schools argue that they have incentives for efficiency and innovation, and that this in turn results in better education. Opposing this view, detractors say that, in reducing costs, for-profit schools tend to also reduce the quality of education and that one cannot allow a desire for profits to take precedence over the quality of a child's education [see Elacqua (2009) for further discussion]. In 2011, in support of the latter view, and in part with the goal of ending forprofit education in Chile, thousands of students rallied through the streets demanding a change in the model of education and better opportunities.Here, we compare the 2006 academic test performance of Chilean students who entered for-profit private high schools and students who entered not-for-profit private high schools. All of these students were in government run primary/middle schools in Santiago in 2004 and subsequently moved to private high schools. We have test scores at baseline in 2004 in language (Spanish), mathematics, natural science and social science, and we have outcome test scores in 2006 in language and mathematics. In addition, we have extensive data about parents and children in 2004, such as the education of the parents, their income, the number of books at home and so on, recorded in an observed covariate x. An obvious concern is that even after adjusting for a high-dimensional observed covariate x, children in different types of schools may differ in terms of some other covariate u that was not observed, and differences in u may bias the comparison.The test scores come from the SIMCE, the Spanish acronym for "System of Measurement of Quality in Education." For the same students, we use test scores for the 8th grade of primary school in 2004 and the second year of high school in 2006. For the typical student, these are test scores at ages 14 and 16. For-profit and not-for-profit are determined by the official definitions of the Chilean IRS based on the institutional identification number (RUT).Do profits boost or depress test scores in similar students? Or are profits irrelevant to test scores? 1.2. Matching for covariate balance, pairing for heterogeneity. To be credible, the comparison must compare children in not-for-profit schools (the treated group) to children similar at baseline in for-profit schools (the control group), and there are many ways the children may differ. It is typically difficult to match closely for all coordinates of a high-dimensional observed covariate x, but it is often not difficult to create matched treated and control groups with similar distributions of x. For instance, if x consisted of 20 binary covariates, it would distinguish 2 20 or about a million categories of students, so it would be very difficult to match thousands of students exactly for all 20 covariates. However, it...

Ballot initiatives allow the public to vote directly on public policy. The literature in political science has attempted to document whether the presence of an initiative can increase voter turnout. We study this question for an initiative that appeared on the ballot in 2008 in Milwaukee, Wisconsin, using a natural experiment based on geography. This form of natural experiment exploits variation in geography where units in one geographic area receive a treatment whereas units in another area do not. When assignment to treatment via geographic location creates as-if random variation in treatment assignment, adjustment for baseline covariates is unnecessary. In many applications, however, some adjustment for baseline covariates may be necessary. As such, analysts may wish to combine identification strategies-using both spatial proximity and covariates. We propose a matching framework to incorporate information about both geographic proximity and observed covariates flexibly which allows us to minimize spatial distance while preserving balance on observed covariates. This framework is also applicable to regression discontinuity designs that are not based on geography. We find that the initiative on the ballot in Milwaukee does not appear to have increased turnout.