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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]

When investigators seek to estimate causal effects, they often assume that selection into treatment is based only on observed covariates. Under this identification strategy, analysts must adjust for observed confounders. While basic regression models have long been the dominant method of statistical adjustment, methods based on matching or weighting have become more common. Of late, methods based on machine learning (ML) have been developed for statistical adjustment. These ML methods are often designed to be black box methods with little input from the researcher. In contrast, matching methods that use covariate prioritization are designed to allow for direct input from substantive investigators. In this article, we use a novel research design to compare matching with covariate prioritization to black box methods. We use black box methods to replicate results from five studies where matching with covariate prioritization was used to customize the statistical adjustment in direct response to substantive expertise. We compare the methods in terms of both point and interval estimation. We conclude with advice for investigators. [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]

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]

Political scientists often turn to natural experiments to draw causal inferences with observational data. Recently, the regression discontinuity design (RD) has become a popular type of natural experiment due to its relatively weak assumptions. We study a special type of regression discontinuity design where the discontinuity in treatment assignment is geographic. In this design, which we call the Geographic Regression Discontinuity (GRD) design, a geographic or administrative boundary splits units into treated and control areas, and analysts make the case that the division into treated and control areas occurs in an as-if random fashion. We show how this design is equivalent to a standard RD with two running variables, but we also clarify several methodological differences that arise in geographical contexts. We also offer a method for estimation of geographically located treatment effects that can also be used to validate the identification assumptions using observable pretreatment characteristics. We illustrate our methodological framework with a re-examination of the effects of political advertisements on voter turnout during a presidential campaign, exploiting the exogenous variation in the volume of presidential ads that is created by media market boundaries. [ABSTRACT FROM PUBLISHER]

BACKGROUND: Little is known about the impact of multimorbidity on long-term outcomes for older emergency general surgery patients. STUDY DESIGN: Medicare beneficiaries, age 65 and older, who underwent operative management of an emergency general surgery condition were identified using Centers for Medicare & Medicaid claims data. Patients were classified as multimorbid based on the presence of a Qualifying Comorbidity Set (a specific combination of comorbid conditions known to be associated with increased risk of in-hospital mortality in the general surgery setting) and compared with those without multimorbidity. Risk-adjusted outcomes through 180 days after discharge from index hospitalization were calculated using linear and logistic regressions. RESULTS: Of 174,891 included patients, 45.5% were identified as multimorbid. Multimorbid patients had higher rates of mortality during index hospitalization (5.9% vs 0.7%, odds ratio [OR] 3.05, p < 0.001) and through 6 months (17.1% vs 3.4%, OR 2.33, p < 0.001) after discharge. Multimorbid patients experienced higher rates of readmission at 1 month (22.9% vs 11.4%, OR 1.48, p < 0.001) and 6 months (38.2% vs 21.2%, OR 1.48, p < 0.001) after discharge, lower rates of discharge to home (42.5% vs 74.2%, OR 0.52, p < 0.001), higher rates of discharge to rehabilitation/nursing facility (28.3% vs 11.3%, OR 1.62, p < 0.001), greater than double the use of home oxygen, walker, wheelchair, bedside commode, and hospital bed (p < 0.001), longer length of index hospitalization (1.33 additional in-patient days, p < 0.001), and higher costs through 6 months ($5,162 additional, p < 0.001). CONCLUSIONS: Older, multimorbid patients experience worse outcomes, including survival and independent function, after emergency general surgery than nonmultimorbid patients through 6 months after discharge from index hospitalization. This information is important for setting recovery expectations for high-risk patients to improve shared decision-making. [ABSTRACT FROM AUTHOR]