Statistical Methods for High-Dimensional Administrative Data

我的研究主要集中在两个方面：首先，我开发和评估统计模型和方法，这些模型和方法可能会导致行政数据库分析中的应用。 第二，我开发了荟萃分析的方法。*从观察数据中估计因果效应的关键挑战是混淆。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包含的变量;不能保证所有必要的混淆因素都已被测量。目标学习（TL）是由货车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，并定制估计过程以有效估计该参数。 TL是计算密集型的，并且其在高维数据中的属性相对未知。** 累积荟萃分析按时间顺序汇总研究信息，总结每次研究后的知识。我们已经开发了频率论和贝叶斯停止规则，使用现有的研究信息，以确定从一个新的研究信息的期望值。** 生存数据的加速失效时间模型具有吸引人的因果推断属性;然而，仍有许多工作要确定其属性。我的NSERC资助的研究在未来五年的目标是：*1。 开发和扩展用于大型管理数据集的有针对性的学习方法。* 2.扩展累积荟萃分析的停止规则方法。* 3.为了扩展生存分析中因果推理的加速失效时间（AFT）模型 * 我的TL重点将是开发计算效率高的算法。 管理数据集通常包括许多与暴露和结局相关性较弱的协变量;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。我将扩展我们的元分析方法。 现有方法使用固定效应似然。扩展方法以解释观察性研究（涉及解决研究间的混杂控制）和多项研究相对简单。 随后的可能性可以使用现有的随机效应方法进行修改。我将通过理论发展、示例数据集和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局方法。我正在研究AFT模型的扩展，以动态治疗方案和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，如这里提出的那些工具，对应用研究的进行至关重要。

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.