摘要翻译：
最近，一种新的边缘结构模型(MSMs)，历史限制型MSMs(HRMSMs)被引入纵向数据，用于定义因果参数，它通常更适合于公共卫生研究，或者至少比MSMs更实用。与MSMS相比，HRMSMs允许研究人员基于固定的、较短的和用户指定的暴露历史来分析治疗对结果的因果影响。默认情况下，后者代表了基于治疗历史的兴趣治疗因果效应，该治疗历史由研究开始和结果收集之间分配的治疗定义。我们在本文中阐述了HRMSMS背后的正式统计框架。除了允许更灵活的因果分析之外，HRMSMs还提高了计算的可处理性，并在设计纵向研究时减轻了统计能力的担忧。在充分的模型假设下，我们给出了HRMSM参数的三种相合估计：治疗加权逆概率(IPTW)、G-计算和双鲁棒(DR)估计。此外，我们还证明了通常用于MSM参数识别和一致估计的假设（反事实的存在性、一致性、时间排序和顺序随机化假设）也导致了HRMSM参数的识别和一致估计。
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英文标题：
《Causal inference in longitudinal studies with history-restricted
  marginal structural models》
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作者：
Romain Neugebauer, Mark J. van der Laan, Marshall M. Joffe, Ira B.
  Tager
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最新提交年份：
2007
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分类信息：

一级分类：Mathematics        数学
二级分类：Statistics Theory        统计理论
分类描述：Applied, computational and theoretical statistics: e.g. statistical inference, regression, time series, multivariate analysis, data analysis, Markov chain Monte Carlo, design of experiments, case studies
应用统计、计算统计和理论统计：例如统计推断、回归、时间序列、多元分析、数据分析、马尔可夫链蒙特卡罗、实验设计、案例研究
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一级分类：Statistics        统计学
二级分类：Methodology        方法论
分类描述：Design, Surveys, Model Selection, Multiple Testing, Multivariate Methods, Signal and Image Processing, Time Series, Smoothing, Spatial Statistics, Survival Analysis, Nonparametric and Semiparametric Methods
设计，调查，模型选择，多重检验，多元方法，信号和图像处理，时间序列，平滑，空间统计，生存分析，非参数和半参数方法
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一级分类：Statistics        统计学
二级分类：Statistics Theory        统计理论
分类描述：stat.TH is an alias for math.ST. Asymptotics, Bayesian Inference, Decision Theory, Estimation, Foundations, Inference, Testing.
Stat.Th是Math.St的别名。渐近，贝叶斯推论，决策理论，估计，基础，推论，检验。
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英文摘要：
  A new class of Marginal Structural Models (MSMs), History-Restricted MSMs (HRMSMs), was recently introduced for longitudinal data for the purpose of defining causal parameters which may often be better suited for public health research or at least more practicable than MSMs \citejoffe,feldman. HRMSMs allow investigators to analyze the causal effect of a treatment on an outcome based on a fixed, shorter and user-specified history of exposure compared to MSMs. By default, the latter represent the treatment causal effect of interest based on a treatment history defined by the treatments assigned between the study's start and outcome collection. We lay out in this article the formal statistical framework behind HRMSMs. Beyond allowing a more flexible causal analysis, HRMSMs improve computational tractability and mitigate statistical power concerns when designing longitudinal studies. We also develop three consistent estimators of HRMSM parameters under sufficient model assumptions: the Inverse Probability of Treatment Weighted (IPTW), G-computation and Double Robust (DR) estimators. In addition, we show that the assumptions commonly adopted for identification and consistent estimation of MSM parameters (existence of counterfactuals, consistency, time-ordering and sequential randomization assumptions) also lead to identification and consistent estimation of HRMSM parameters.
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PDF链接：
https://arxiv.org/pdf/705.127