Journal of the American Statistical Association

最新文章

• Discussion of Regression Models for Understanding COVID-19 Epidemic Dynamics With Incomplete Data AI翻译
  作者：Dean, Natalie; Yang, Yang
  作者单位：Emory University; State University System of Florida; University of Florida
• Introduction to Theory and Methods Special Issue on Precision Medicine and Individualized Policy Discovery Part II AI翻译
  作者：[Anonymous]
• Smooth Backfitting of Proportional Hazards With Multiplicative Components AI翻译
  作者：Hiabu, Munir; Mammen, Enno; Dolores Martinez-Miranda, M.; Nielsen, Jens P.
  作者单位：University of Sydney; Ruprecht Karls University Heidelberg; University of Granada; City St Georges, University of London
  摘要：Smooth backfitting has proven to have a number of theoretical and practical advantages in structured regression. By projecting the data down onto the structured space of interest smooth backfitting provides a direct link between data and estimator. This article introduces the ideas of smooth backfitting to survival analysis in a proportional hazard model, where we assume an underlying conditional hazard with multiplicative components. We develop asymptotic theory for the estimator. In a compre...
• Back to Our Future: Text Analytics Insights AI翻译
  作者：Martinez, Wendy L.
  摘要：Each year, the Journal of the American Statistical Association (ASA) publishes the presidential address from the Joint Statistical Meetings (JSM). Here, we present the 2020 address verbatim save for the addition of references and a few minor editorial corrections.
• Recurrent Events Analysis With Data Collected at Informative Clinical Visits in Electronic Health Records AI翻译
  作者：Sun, Yifei; McCulloch, Charles E.; Marr, Kieren A.; Huang, Chiung-Yu
  作者单位：Columbia University; University of California System; University of California San Francisco; Johns Hopkins University
  摘要：Although increasingly used as a data resource for assembling cohorts, electronic health records (EHRs) pose many analytic challenges. In particular, a patient's health status influences when and what data are recorded, generating sampling bias in the collected data. In this article, we consider recurrent event analysis using EHR data. Conventional regression methods for event risk analysis usually require the values of covariates to be observed throughout the follow-up period. In EHR databases...
• Modeling and Regionalization of China's PM2.5 Using Spatial-Functional Mixture Models AI翻译
  作者：Liang, Decai; Zhang, Haozhe; Chang, Xiaohui; Huang, Hui
  作者单位：Peking University; Peking University; Nankai University; Microsoft; Oregon State University; Sun Yat Sen University
  摘要：Severe air pollution affects billions of people around the world, particularly in developing countries such as China. Effective emission control policies rely primarily on a proper assessment of air pollutants and accurate spatial clustering outcomes. Unfortunately, emission patterns are difficult to observe as they are highly confounded by many meteorological and geographical factors. In this study, we propose a novel approach for modeling and clustering PM2.5 concentrations across China. We ...
• Robust Q-Learning AI翻译
  作者：Ertefaie, Ashkan; McKay, James R.; Oslin, David; Strawderman, Robert L.
  作者单位：University of Rochester; University of Pennsylvania; University of Pennsylvania; University of Pennsylvania; University of Pennsylvania
  摘要：Q-learning is a regression-based approach that is widely used to formalize the development of an optimal dynamic treatment strategy. Finite dimensional working models are typically used to estimate certain nuisance parameters, and misspecification of these working models can result in residual confounding and/or efficiency loss. We propose a robust Q-learning approach which allows estimating such nuisance parameters using data-adaptive techniques. We study the asymptotic behavior of our estima...
• Learning Individualized Treatment Rules for Multiple-Domain Latent Outcomes AI翻译
  作者：Chen, Yuan; Zeng, Donglin; Wang, Yuanjia
  作者单位：Columbia University; University of North Carolina; University of North Carolina Chapel Hill
  摘要：For many mental disorders, latent mental status from multiple-domain psychological or clinical symptoms may perform as a better characterization of the underlying disorder status than a simple summary score of the symptoms, and they may also serve as more reliable and representative features to differentiate treatment responses. Therefore, to address the complexity and heterogeneity of treatment responses for mental disorders, we provide a new paradigm for learning optimal individualized treat...
• A Penalized Regression Framework for Building Polygenic Risk Models Based on Summary Statistics From Genome-Wide Association Studies and Incorporating External Information AI翻译
  作者：Chen, Ting-Huei; Chatterjee, Nilanjan; Landi, Maria Teresa; Shi, Jianxin
  作者单位：Laval University; Johns Hopkins University; National Institutes of Health (NIH) - USA; NIH National Cancer Institute (NCI); NIH National Cancer Institute- Division of Cancer Epidemiology & Genetics; National Institutes of Health (NIH) - USA; NIH National Cancer Institute (NCI); NIH National Cancer Institute- Division of Cancer Epidemiology & Genetics
  摘要：Large-scale genome-wide association studies (GWAS) provide opportunities for developing genetic risk prediction models that have the potential to improve disease prevention, intervention or treatment. The key step is to develop polygenic risk score (PRS) models with high predictive performance for a given disease, which typically requires a large training dataset for selecting truly associated single nucleotide polymorphisms (SNPs) and estimating effect sizes accurately. Here, we develop a com...
• Combining Multiple Observational Data Sources to Estimate Causal Effects AI翻译
  作者：Yang, Shu; Ding, Peng
  作者单位：North Carolina State University; University of California System; University of California Berkeley
  摘要：The era of big data has witnessed an increasing availability of multiple data sources for statistical analyses. We consider estimation of causal effects combining big main data with unmeasured confounders and smaller validation data withon these confounders. Under the unconfoundedness assumption with completely observed confounders, the smaller validation data allow for constructing consistent estimators for causal effects, but the big main data can only give error-prone estimators in general....