IMPORTANCEWhile left ventricular assist devices (LVADs) increase survival for patients with advanced heart failure (HF), racial and sex access and outcome inequities remain and are poorly understood. OBJECTIVES To assess risk-adjusted inequities in access and outcomes for both Black and female patients and to examine heterogeneity in treatment decisions among patients for whom clinician discretion has a more prominent role. DESIGN, SETTING, AND PARTICIPANTS This retrospective cohort study of 12 310 Medicare beneficiaries used 100% Medicare Fee-for-Service administrative claims. Included patients had been
In the recent literature on estimating heterogeneous treatment effects, each proposed method makes its own set of restrictive assumptions about the intervention's effects and which subpopulations to explicitly estimate. Moreover, the majority of the literature provides no mechanism to identify which subpopulations are the most affected-beyond manual inspection-and provides little guarantee on the correctness of the identified subpopulations. Therefore, we propose Treatment Effect Subset Scan (TESS), a new method for discovering which subpopulation in a randomized experiment is most significantly affected by a treatment. We frame this challenge as a pattern detection problem where we efficiently maximize a nonparametric scan statistic over subpopulations. Furthermore, we identify the subpopulation which experiences the largest distributional change as a result of the intervention, while making minimal assumptions about the intervention's effects or the underlying data generating process. In addition to the algorithm, we demonstrate that the asymptotic Type I and II error can be controlled, and provide sufficient conditions for detection consistency-i.e., exact identification of the affected subpopulation. Finally, we validate the efficacy of the method by discovering heterogeneous treatment effects in simulations and in real-world data from a well-known program evaluation study.Keywords: causal inference, program evaluation, algorithms, distributional average treatment effect, treatment effect subset scan, heterogeneous treatment effects 1 arXiv:1803.09159v2 [stat.ME]
Advances in medical imaging technology have created opportunities for computer-aided diagnostic tools to assist human practitioners in identifying relevant patterns in massive, multiscale digital pathology slides. This work presents Hierarchical Linear Time Subset Scanning, a novel statistical method for pattern detection. Hierarchical Linear Time Subset Scanning exploits the hierarchical structure inherent in data produced through virtual microscopy in order to accurately and quickly identify regions of interest for pathologists to review. We take a digital image at various resolution levels, identify the most anomalous regions at a coarse level, and continue to analyze the data at increasingly granular resolutions until we accurately identify its most anomalous subregions. We demonstrate the performance of our novel method in identifying cancerous locations on digital slides of prostate biopsy samples and show that our methods detect regions of cancer in minutes with high accuracy, both as measured by the ROC curve (measuring ability to distinguish between benign and cancerous slides) and by the spatial precision-recall curve (measuring ability to pick out the malignant areas on a slide which contains cancer). Existing methods need small scale images (small areas of a slide preselected by the pathologist for analysis, eg, 32 × 32 pixels) and may not work effectively on large, raw digitized images of size 100K × 100K pixels. In this work, we provide a methodology to fill this significant gap by analyzing large digitized images and identifying regions of interest that may be indicative of cancer.
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