Statistical Methods for High-Dimensional Administrative Data

我的研究主要集中在两个方面：第一，我开发和评估统计模型和方法，这些模型和方法可能导致在行政数据库分析中的应用。第二，我开发了荟萃分析的方法。从观测数据估计因果效应的关键挑战是令人困惑的。在使用管理数据时，混淆是一个特别的挑战，因为研究人员无法控制数据集中的变量；不能保证所有必要的混杂因素都已被测量。*定向学习(TL)是van der Laan开发的一个框架，用于估计因果效应，将推理集中在目标参数上，并为有效估计该参数而调整估计过程。TL是计算密集型的，其在高维数据中的性质相对未知。*累积荟萃分析按时间顺序汇总研究信息，总结每项研究后的知识。我们已经开发了频率停止规则和贝叶斯停止规则，它们使用来自现有研究的信息来确定来自新研究的信息的期望值。*生存数据的加速失效时间模型具有吸引人的因果推理性质；然而，仍有许多工作需要确定它们的性质。*我在未来五年由NSERC资助的研究的目标是：*1.开发和扩展用于大型管理数据集的定向学习方法。*2.扩展用于累积Meta分析的停止规则方法。*3.扩展用于生存分析因果推断的加速失效时间(AFT)模型*我与TL的重点将是开发计算高效的算法。管理数据集通常包括许多与暴露和结果有弱关联的协变量；总结可能有用。我将开发计算效率高的算法来扩展目标学习框架。我将主要通过理论发展和模拟来评估这些方法。*我将扩展我们的荟萃分析方法。现有的方法使用固定效果似然法。将方法扩展到观察性研究(涉及处理不同研究之间的混淆控制)和多项研究是相对简单的。随后的可能性可以使用随机效果的现有方法进行修改。我将通过理论发展、通过实例数据集和通过模拟来评估这些方法。*最后，AFT模型是相对未被充分利用但高度可解释的生存结果的方法。我正在致力于将AFT模型扩展到动态治疗方案并模拟灵活的功能形式。*这些结果将对研究产生重大影响。开发和仔细、严格地评估统计工具，如本文提出的工具，对开展应用研究至关重要。

My research addresses two main foci: first, I develop and evaluate statistical models and methods that may lead to applications in the analysis of administrative databases. Second, I develop methods for meta-analysis.******The critical challenge in estimating causal effects from observational data is confounding. Confounding is a particular challenge when using administrative data, because the researcher does not have control over variables included in the dataset; there is no guarantee that all necessary confounders have been measured.******Targeted learning (TL) is a framework developed by van der Laan to estimate causal effects, focusing inference on a target parameter and tailoring the estimation process for efficient estimation of that parameter. TL is computationally intensive, and its properties in high-dimensional data are relatively unknown. ******Cumulative meta-analysis aggregates information from studies in chronological order, summarizing knowledge after each study. We have developed frequentist and Bayesian stopping rules which use information from the existing studies to determine the expected value of information from a new study. ******Accelerated failure time models for survival data have appealing properties for causal inference; however, much work remains to determine their properties.******The objectives of my NSERC-funded research over the next five years are:***1. To develop and extend methods for targeted learning for use with large administrative datasets.***2. To extend stopping-rule methods for cumulative meta-analysis.***3. To extend accelerated failure time (AFT) models for causal inference in survival analysis******My focus with TL will be on developing computationally efficient algorithms. Administrative datasets often include many covariates with weak associations with exposure and outcome; summaries may be useful. I will develop computationally efficient algorithms to extend the targeted learning framework. I will evaluate these methods primarily via theoretical development and via simulation.******I will extend our meta-analytic methods. Existing methods use the fixed-effects likelihood. It is relatively straightforward to extend methods to account for observational studies (which involves addressing confounding control across studies), and multiple studies. The subsequent likelihood can be modified using existing methods for random effects. I will evaluate these methods through theoretical development, through example datasets, and via simulation.******Finally, AFT models are relatively under-utilized but highly interpretable methods for survival outcomes. I am working on extensions of AFT models to dynamic treatment regimes and to model flexible functional forms.******These results will have significant impact on research. Development and careful, rigorous evaluation of statistical tools, such as those proposed here, is essential to the conduct of applied research.