Statistical Inference for Novel Study Designs

现代研究需要现代统计方法。研究设计是为研究问题收集数据所使用的一套方法和程序。为了调查因果关系，生物医学和社会科学的现代研究人员使用越来越复杂的研究设计来减少混杂偏差，提高统计效率，和/或满足后勤或伦理约束。在这些新颖的研究设计中收集的研究数据通常是通过基于模型的方法进行分析的，这些方法假设数据是从某些统计模型中产生的。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方法来阐明使用实验和观测数据的因果推理。它们已经成功地应用于理解经典的研究设计，如完全随机实验、横断面队列研究和工具变量。然而，因果推理理论与涉及复杂干预和数据收集过程的现代研究实践之间仍存在很大差距。许多有价值的数据集仍在用劣质的、容易出错的方法进行分析，这是几个科学学科中正在发生的复制危机的一个根本问题。本计画将调查两种受欢迎程度呈指数增长的新颖研究设计的统计推断。第一个设计是家族内孟德尔随机化。孟德尔随机化是流行病学和遗传学中使用遗传变异作为工具变量的一种流行方法。直觉上，遗传变异在受孕期间是随机的，应该独立于任何混杂因素。然而，如果没有控制家庭影响，这是不正确的。该项目将为孟德尔随机化的因果推理如何精确地基于减数分裂（生殖细胞的分裂）的随机化提供严格的理由，并提出在数据中使用家族结构的新方法。新方法将应用于大型遗传数据集，以检查健康研究中的最新发现。本项目研究的第二个设计是阶梯式楔形试验。在该设计中，试验参与者逐渐从对照条件过渡到治疗条件，并且过渡时间是随机的。目前，阶梯式楔形试验通常通过线性混合效应模型进行分析，但最近的研究表明，这对建模假设非常敏感。这个项目将开发完全基于随机化的新方法。这将大大提高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.