Statistical Inference for Novel Study Designs

现代研究呼唤现代统计方法。研究设计是为研究问题收集数据时使用的一套方法和程序。为了调查因果关系，现代生物医学和社会科学的研究人员使用越来越复杂的研究设计来减少混淆的偏见，提高统计效率，和/或满足后勤或伦理限制。在这些新颖的研究设计中收集的研究数据通常通过基于模型的方法进行分析，这些方法假定数据是从某些统计模型产生的。因此，这类研究的结果可能对建模假设非常敏感。最近的发展提供了一套使用实验和观察数据来解释因果推断的方法。它们已被成功地应用于理解经典的研究设计，如完全随机的实验、横断面队列研究和工具变量。然而，因果推理的理论与现代研究实践之间仍有很大差距，这些研究涉及复杂的干预和数据收集过程。许多有价值的数据集仍在通过劣质的、容易出错的方法进行分析，这是几个科学学科中正在进行的复制危机的根本问题。本项目将研究两个新的研究设计中的统计推断，这些设计的受欢迎程度呈指数级增长。第一个设计是家庭内的孟德尔随机化。孟德尔随机化是流行病学和遗传学中一种流行的方法，它使用遗传变异作为工具变量。直觉上，基因变异在受孕期间是随机的，应该不受任何混杂因素的影响。然而，如果不考虑家庭影响，这就不是真的了。这个项目将提供一个严格的理由，证明孟德尔随机化中的因果推断如何准确地基于减数分裂(生殖细胞的分裂)的随机化，并提出使用数据中的家族结构的新方法。新方法将应用于大型遗传数据集，以检验健康研究中的最新发现。该项目中调查的第二个设计是阶梯式楔形试验。在这一设计中，试验参与者逐渐从对照条件交叉到治疗条件，交叉时间是随机的。目前，阶梯式楔形试验通常用线性混合效应模型进行分析，但最近的研究表明，这对建模假设极其敏感。这个项目将开发完全基于随机化的新方法。这将大大增强阶梯式楔形试验的稳健性。

Modern research studies call for modern statistical methodologies. A study design is the set of methods and procedures used in collecting data for a research problem. To investigate causal relationships, modern researchers in biomedical and social sciences use increasingly complex study designs to reduce confounding bias, increase statistical efficiency, and/or to meet logistical or ethical constraints. Research data collected in these novel study designs are often being analysed by model-based approaches, which assume the data are generated from certain statistical models. As a consequence, the results of such studies can be very sensitive to the modelling assumptions.Recent developments have provided a suite of methods to elucidate causal inference using experimental and observational data. They have been successfully applied to understand classical study designs such as completely randomised experiments, cross-sectional cohort studies, and instrumental variables. However, there is still a significant gap between the theory of causal inference and the practice of modern research studies involving complex processes of intervention and data collection. Many valuable datasets are still being analysed by inferior, error-prone methods, which is a fundamental issue for the ongoing replication crisis in several scientific disciplines.This project will investigate statistical inference in two novel study designs with exponentially increasing popularity. The first design is within-family Mendelian randomisation. Mendelian randomisation is a popular method in epidemiology and genetic that uses genetic variants as instrumental variables. Intuitively, genetic variants are randomised during conception and should be independent of any confounders. However, this is not true without controlling for family effects. This project will provide a rigorous justification of how the causal inference in Mendelian randomisation can be based precisely on the randomisation in meiosis (the division of germ cells) and propose new methods that use the family structure in the data. The new methods will be applied to large genetic datasets to examine recent findings in health research.The second design investigated in this project is the stepped wedge trials. In this design, the trial participants gradually cross over from the control condition to the treatment, and the cross-over times are randomised. Currently, stepped wedge trials are routinely analysed by linear mixed-effect models, but recent works have demonstrated that this is extremely sensitive to the modelling assumptions. This project will develop new methods that are exactly based on randomisation. This will greatly enhance the robustness of stepped wedge trials.