Statistical Methods for High-Dimensional Administrative Data

我的研究涉及两个主要焦点：首先，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管理数据库分析中的应用。其次，我发展了元分析的方法。******根据观测数据估计因果关系的关键挑战是混淆的。当使用管理数据时，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；不能保证已经测量了所有必要的混杂因素。******目标学习（TL）是由van der Laan开发的一个框架，用于估计因果关系，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，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，在每次研究后总结知识。我们已经开发了频率和贝叶斯停止规则，它们使用现有研究的信息来确定新研究信息的期望值。******加速失效时间模型的生存数据有吸引力的性质，因果推理；然而，要确定它们的性质还有很多工作要做。******我在未来五年的nserc资助的研究目标是：***1。开发和扩展用于大型管理数据集的针对性学习方法。扩展累积元分析的停止规则方法。***为了扩展加速失效时间（AFT）模型用于生存分析中的因果推理******我在TL中的重点将放在开发计算效率高的算法上。管理数据集通常包括许多协变量，与暴露和结果的关联较弱；摘要可能有用。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仿真来评估这些方法。******我将扩展我们的元分析方法。现有方法使用固定效应似然。扩展方法来解释观察性研究（涉及处理跨研究的混淆控制）和多个研究是相对简单的。后续的似然可以使用随机效应的现有方法进行修改。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模型是相对未充分利用但可高度解释的生存结果方法。我正在研究将AFT模型扩展到动态治疗制度和建模灵活的功能形式。******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，例如这里提出的这些工具，对应用研究的开展至关重要。

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.