论文检索

大规模面板中异质性与协整的推断理论AI 解读

This paper provides an estimation and testing framework to assess the presence and the extent of slope heterogeneity and cointegration when the units are a mixture of spurious and/or cointegrating regressions. We propose two moment estimators for the degree of heterogeneity (measured by the dispersion of the slope coefficients around their average) and for the fraction of spurious regressions, which are found to be consistent in the whole parameter space. Based on these…

多层模型估计的计量经济学方法AI 解读

In this paper we consider “multidimensional” or “hierarchical” or “multilevel” models that are popular in the educational and economics literatures. Instead of two levels (individuals over time in the standard panel data model), we now have multiple levels (e.g. students in classrooms in schools in districts). We apply standard methods of analysis for econometric panel data to multilevel models. Specifically, we generalize the results of Hausman and Taylor and the subsequent…

具有系统性斜率变化的异质面板估计AI 解读

We analyse estimation procedures for the panel data models with heterogeneous slopes. Specifically we take into account a possible dependence between regressors and heterogeneous slope coefficients, which is referred to as systematic variation. It is shown that under relevant forms of systematic slope variations (i) the pooled OLS estimator is severely biased, (ii) Swamy’s GLS estimator is inconsistent if the number of time periods T is fixed, whereas (iii) the mean-group…

变维模型的最优辅助先验与可逆跳跃提议AI 解读

This article develops a Markov chain Monte Carlo (MCMC) method for a class of models that encompasses finite and countable mixtures of densities and mixtures of experts with a variable number of mixture components. The method is shown to maximize the expected probability of acceptance for cross-dimensional moves and to minimize the asymptotic variance of sample average estimators under certain restrictions. The method can be represented as a retrospective sampling algorithm…

处理状态误分类下的工具变量分位数回归AI 解读

Abstract This article investigates the instrumental variable quantile regression model (Chernozhukov and Hansen, 2005, Econometrica 73, 245–261; 2013, Annual Review of Economics , 5, 57–81) with a binary endogenous treatment. It offers two identification results when the treatment status is not directly observed. The first result is that, remarkably, the reduced-form quantile regression of the outcome variable on the instrumental variable provides a lower bound on the…

反事实世界中的个体化决策机器智能：作者回复AI 解读

This JASA rejoinder concerns the problem of individualized decision making under point, sign, and partial identification. The paper unifies various classical decision making strategies through a lower bound perspective proposed in Cui and Tchetgen Tchetgen (2020b) in the context of optimal treatment regimes under uncertainty due to unmeasured confounding. Building on this unified framework, the paper also provides a novel minimax solution (i.e., a rule that minimizes the…

利用高维协变量特异性治疗效应曲线估计最优个体化治疗规则AI 解读

With a large number of baseline covariates, we propose a new semiparametric modeling strategy for heterogeneous treatment effect estimation and individualized treatment selection, which are two major goals in personalized medicine. We achieve the first goal through estimating a covariate-specific treatment effect (CSTE) curve modeled as an unknown function of a weighted linear combination of all baseline covariates. The weight or the coefficient for each covariate is…

无直接效应假设下最优策略的有效估计AI 解读

Lin Liua , Zach Shahnb, James M. Robinsc* & Andrea Rotnitzkyda Institute of Natural Sciences, School of Mathematical Sciences, MOE-LSC and SJTU-Yale Center for Biostatistics and Data Science, Shanghai Jiao Tong University, Shanghai, China; b IBM Research, Yorktown Heights, NY; c Department of Biostatistics and Epidemiology, Harvard University, Boston, MA; d Department of Economics, Universidad Torcuato Di Tella and CONICET, Buenos Aires, ArgentinaSupplementary materials for…

BAGS：具有亚组特异生存比较的贝叶斯自适应群序贯试验设计AI 解读

Ruitao Lin, Peter F. Thall* & Ying YuanDepartment of Biostatistics, The University of Texas MD Anderson Cancer Center, Houston, TXSupplementary materials for this article are available online. Please go to www.tandfonline.com/r/JASA.CONTACT Peter F. Thall rex@mdanderson.org Department of Biostatistics, The University of Texas MD Anderson Cancer Center, Houston, TA 77030.AbstractA Bayesian group sequential design is proposed that performs survival comparisons within patient…

学习何时开始治疗的动态政策AI 解读

Many applied decision-making problems have a dynamic component: The policymaker needs not only to choose whom to treat, but also when to start which treatment. For example, a medical doctor may choose between postponing treatment (watchful waiting) and prescribing one of several available treatments during the many visits from a patient. We develop an “advantage doubly robust” estimator for learning such dynamic treatment rules using observational data under the assumption…

用于估计最优动态治疗策略的随机树搜索AI 解读

Yilun Sunab & Lu Wanga*a Department of Biostatistics, University of Michigan, Ann Arbor, MI; b Department of Radiation Oncology, University of Michigan, Ann Arbor, MISupplementary materials for this article are available online. Please go to www.tandfonline.com/r/JASA.CONTACT Lu Wang luwang@umich.edu Department of Biostatistics, University of Michigan, Ann Arbor, MI 48109.AbstractA dynamic treatment regime (DTR) is a sequence of decision rules that adapt to the time-varying…

针对多领域潜在结局的个体化治疗规则学习AI 解读

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, in order to address the complexity and heterogeneity of treatment responses for mental disorders, we provide a new paradigm for learning…

长期平均结果的离策略估计及其移动健康应用AI 解读

Due to the recent advancements in wearables and sensing technology, health scientists are increasingly developing mobile health (mHealth) interventions. In mHealth interventions, mobile devices are used to deliver treatment to individuals as they go about their daily lives. These treatments are generally designed to impact a near time, proximal outcome such as stress or physical activity. The mHealth intervention policies, often called just-in-time adaptive interventions,…

从半连续结局估计个体化处理规则的两部分框架AI 解读

Jared D. Hulinga , Maureen A. Smithb & Guanhua Chenc* a Division of Biostatistics, University of Minnesota, Minneapolis, MN; b Departments of Population Health Sciences and Family Medicine, University of Wisconsin-Madison, Madison, WI; c Department of Biostatistics and Medical Informatics, University of Wisconsin-Madison, Madison, WISupplementary materials for this article are available online. Please go to www.tandfonline.com/r/JASA.CONTACT Jared Huling huling@umn.edu…

学习最优个体化治疗规则的改进双重稳健估计AI 解读

Individualized treatment rules (ITRs) recommend treatment according to patient characteristics. There is a growing interest in developing novel and efficient statistical methods in constructing ITRs. We propose an improved doubly robust estimator of the optimal ITRs. The proposed estimator is based on a direct optimization of an augmented inverse-probability weighted estimator (AIPWE) of the expected clinical outcome over a class of ITRs. The method enjoys two key…

利用纵向移动健康数据学习个性化干预策略AI 解读

Personalized policy represents a paradigm shift from one-decision-rule-for-all users to an individualized decision rule for each user. Developing personalized policy in mobile health applications imposes challenges. First, for lack of adherence, data from each user are limited. Second, unmeasured contextual factors can potentially impact on decision making. Aiming to optimize immediate rewards, we propose using a generalized linear mixed modeling framework where population…

内生性下最优治疗规则的半参数工具变量方法AI 解读

There is a fast-growing literature on estimating optimal treatment regimes based on randomized trials or observational studies under a key identifying condition of no unmeasured confounding. Because confounding by unmeasured factors cannot generally be ruled out with certainty in observational studies or randomized trials subject to noncompliance, we propose a general instrumental variable (IV) approach to learning optimal treatment regimes under endogeneity. Specifically,…

利用观察数据估计与验证比值型条件平均处理效应AI 解读

While sample sizes in randomized clinical trials are large enough to estimate the average treatment effect well, they are often insufficient for estimation of treatment-covariate interactions critical to studying data-driven precision medicine. Observational data from real world practice may play an important role in alleviating this problem. One common approach in trials is to predict the outcome of interest with separate regression models in each treatment arm, and…

稳健 Q 学习AI 解读

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…

最优给药策略的双重稳健估计AI 解读

The goal of precision medicine is to tailor treatment strategies on an individual patient level. Although several estimation techniques have been developed for determining optimal treatment rules, the majority of methods focus on the case of a dichotomous treatment, an example being the dynamic weighted ordinary least squares regression approach of Wallace and Moodie. We propose an extension to the aforementioned framework to allow for a continuous treatment with the…

利用工具变量方法构建最优个体化决策规则AI 解读

There is an extensive literature on the estimation and evaluation of optimal individualized treatment rules in settings where all confounders of the effect of treatment on outcome are observed. We study the development of individualized decision rules in settings where some of these confounders may not have been measured but a valid binary instrument is available for a binary treatment. We first consider individualized treatment rules, which will naturally be most…

三治疗组因果效应的三元匹配：创伤中心等级与死亡率比较AI 解读

Giovanni Nattinoa, Bo Lua*, Junxin Shib , Stanley Lemeshowa & Henry Xiangbca Division of Biostatistics, College of Public Health, The Ohio State University, Columbus, OHb Center for Pediatric Trauma Research, Center for Injury Research and Policy, Abigail Wexner Research Institute at Nationwide Children’s Hospital, Columbus, OHc College of Medicine, The Ohio State University, Columbus, OHSupplementary materials for this article are available online. Please go to…

存在未测量混杂时个体化治疗规则的选择与排序AI 解读

It is common to compare individualized treatment rules based on the value function, which is the expected potential outcome under the treatment rule. Although the value function is not point-identified when there is unmeasured confounding, it still defines a partial order among the treatment rules under Rosenbaum’s sensitivity analysis model. We first consider how to compare two treatment rules with unmeasured confounding in the single-decision setting and then use this…

分位数回归的平滑方法AI 解读

We propose to smooth the objective function, rather than only the indicator on the check function, in a linear quantile regression context. Not only does the resulting smoothed quantile regression estimator yield a lower mean squared error and a more accurate Bahadur–Kiefer representation than the standard estimator, but it is also asymptotically differentiable. We exploit the latter to propose a quantile density estimator that does not suffer from the curse of…