Developing methods for model selection in causal health analyses.

开发因果健康分析中模型选择的方法。

• 批准号: 2741534
• 金额: --
• 依托单位: University of Bristol
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2022
• 资助国家: 英国
• 起止时间: 2022 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2741534
• 关键词: Developing; methods; model; selection; causal

This PhD project aims to conceptualise and implement an automated Bayesian system that can make decisions about statistical modelling in the context of causal health - these decisions include model form selection, variable selection, parameter fitting techniques and model evaluation methods / success metrics. Present developments are looking into causal inference, specifically the role of confounders and mediators in variable selection. We investigate different variable selection rubrics and confounder selection strategies to assess the suitability of different methods at forming causally valid statistical models. We seek to, from here, consider attempts at classifying variables (confounders, mediators or other types / properties) using Bayesian weighting where such categorisations are both subject to real-world interpretation and not typically identifiable from within the data. The broader project requires making use of larger more complicated data sources, plus priors for key variables. Lastly, we consider taking into account missing-ness (categorising types of missing data such as MCAR, MAR and MNAR), measurement error, variable categorisation and the underlying causal diagram (DAGs). The novelty of the research arises from the consideration of effect-interactions between variables and use of novel Bayesian techniques. The intended impact of the research would be on statistical models and their evaluation within medical papers, such as randomized control trials for new pharmaceuticals, wherein one typically employs a unit-block treatment model as a means of measuring treatment effects of different medicines on patients. This would improve the strength and statistical validity of papers in the medical literature, in turn improving decision making by medical practitioners in the real-world, thus improving patient outcomes. This project falls within the EPSRC mathematical sciences research area with a goal towards transforming health and healthcare. This project is supervised jointly by three professors: Kate Tilling (University of Bristol, Bristol Medical School), Jonathan Sterne (University of Bristol, Bristol Medical School) and Rhian Daniels (University of Cardiff, Department of Statistics). This PhD is funded jointly from three sources: 25% by NHS BRISTOL, 50% by EPSCRC COMPASS CDT, 25% by Faculty Funded PGR Studentship CDT funding. The project does not have industry partners as such, but Emma has worked alongside Southmead Hospital Bristol as part of her research.

该博士项目旨在概念化和实现一个自动贝叶斯系统，该系统可以在因果健康的背景下做出关于统计建模的决策-这些决策包括模型形式选择，变量选择，参数拟合技术和模型评估方法/成功指标。目前的发展正在寻找因果推理，特别是在变量选择的混杂因素和中介的作用。我们研究了不同的变量选择规则和混杂因素选择策略，以评估不同方法在形成因果关系有效的统计模型时的适用性。从这里开始，我们试图考虑使用贝叶斯加权对变量（混杂因素，中介或其他类型/属性）进行分类的尝试，这些分类都受到现实世界的解释，并且通常无法从数据中识别。更广泛的项目需要利用更大更复杂的数据源，加上关键变量的先验知识。最后，我们考虑考虑缺失（缺失数据的分类类型，如MCAR、MAR和MNAR）、测量误差、变量分类和潜在因果图（DAG）。该研究的新奇来自于对变量之间的效应交互作用的考虑和新贝叶斯技术的使用。该研究的预期影响将是对统计模型及其在医学论文中的评估，例如新药的随机对照试验，其中通常采用单位区组治疗模型作为测量不同药物对患者的治疗效果的手段。这将提高医学文献中论文的强度和统计有效性，从而改善现实世界中医疗从业者的决策，从而改善患者的预后。该项目属于EPSRC数学科学研究领域的福尔斯，目标是改变健康和医疗保健。该项目由三位教授共同监督：Kate Tilling（布里斯托大学，布里斯托医学院）、Jonathan Sterne（布里斯托大学，布里斯托医学院）和Rhian Daniels（卡迪夫大学，统计系）。该博士学位由三个来源共同资助：25%由NHS布里斯托资助，50%由EPSCRC COMPASS CDT资助，25%由教师资助的PGR助学金CDT资助。该项目没有这样的行业合作伙伴，但艾玛一直与绍斯米德医院布里斯托作为她的研究的一部分。