A general framework to adjust for missing confounders in observational studies

调整观察性研究中缺失的混杂因素的通用框架

• 批准号: MR/M025195/1
• 负责人: Marta Blangiardo
• 金额: $ 41.16万
• 依托单位: Imperial College London
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2015
• 资助国家: 英国
• 起止时间: 2015 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=MR%2FM025195%2F1
• 关键词: general; framework; adjust; missing; confounders

Assessing the impact of a risk factor/exposure X on a health outcome Y in observational studies is invariably subject to confounding issues. Cohort studies are an ideal source of information as they typically contain a rich set of individual level variables. Nevertheless a study based only on a cohort may suffer from problems of selection bias and lack of population representativeness. Cohort studies may also lack statistical power to assess rare outcomes, and geographical or other group-level variations which limits the extent to which contextual factors such as area level social deprivation can be investigated. Routinely collected administrative data are a good alternative in terms of representativeness; however, these data sources typically have a limited number of variables for a large population, and might miss important predictors/confounders leading to potentially biased estimation of the risks.We propose a general framework that integrating these two sources of data takes advantage of the detailed information on confounders from cohorts/surveys and benefits from the statistical power and population representativeness of the registries. This strategy entails missing data imputation as administrative datasets contain data on each individual in the target population, while cohorts/surveys typically cover only a subset of individuals, so that the confounders obtained from the latter source will be partially measured (i.e. will be missing for some of the units in the registries). Imputing each single confounder could prove computationally unfeasible and constrained to several assumptions given the potentially large number of confounders to consider.We will build a propensity score like index (which we will call Partial Propensity Score - PPS) to summarise the values of the confounders from the cohorts/surveys so we will need to impute only one variable when missing. Through a flexible model the index will be included in the epidemiological analysis and we will be able to provide a direct estimate of the causal link between X and Y as all the confounders have been taken into account.We will build our framework first on individual level data and then extend it to aggregated level, e.g. small area studies generally used to summarise spatial and spatio-temporal variations in epidemiological risks (e.g. for disease surveillance) or to focus on aetiological questions (e.g. to unveil environmental/social determinant of mortality or morbidity). We will use Bayesian full probability modelling which provides a flexible approach of incorporating different assumptions about the missing data mechanism and accommodating different patterns of missing data, and through realistic simulation studies we will evaluate the properties of the framework and compare it with other state-of-the-art methods. In addition two real case studies will be considered. The first will assess the risk of low birth weight given exposure to chlorine in water in Northern England and will be based on individual level data. The second will investigate the impact of air pollution concentration and noise exposure on hospital admissions from cardiovascular causes in England and Wales and will be at the small area level. Through the case studies we will be able to unveil how our proposed methodology changes the results of epidemiological analyses in terms of the effect of exposure on the health outcomes, compared to the commonly used analysis based on data from population registries only. This will have the potential of translating into changes in health policies and strategies to take into account the improved, more accurate results and could become the new state-of-the-art method for analysis of observational studies.

在观察性研究中，评估风险因素/暴露X对健康结果Y的影响总是会遇到令人困惑的问题。队列研究是一个理想的信息来源，因为它们通常包含一组丰富的个体水平变量。然而，仅以队列为基础的研究可能会遇到选择偏差和缺乏总体代表性的问题。队列研究也可能缺乏统计能力来评估罕见的结果，以及地理或其他群体层面的差异，这限制了地区层面的社会剥夺等背景因素可以被调查的程度。就代表性而言，常规收集的行政数据是一个很好的替代方案；然而，对于大量人口来说，这些数据来源的变量数量通常有限，可能会错过重要的预测因素/混杂因素，从而导致对风险的潜在偏见估计。我们提出了一个一般框架，即整合这两个数据来源，利用来自队列/调查的混杂因素的详细信息，并受益于登记的统计能力和人口代表性。由于行政数据集包含目标人口中每个人的数据，而队列/调查通常只涵盖个人的一个子集，因此，从后一个来源获得的混杂因素将被部分衡量(即，登记册中的一些单位将会缺失)，因此这一战略需要缺失数据。考虑到要考虑的潜在大量混杂因素，对每个单独的混杂因素进行计算可能被证明是不可行的，并且受到几个假设的限制。我们将建立一个类似于指数的倾向得分(我们将其称为部分倾向得分-PPS)来总结来自队列/调查的混杂因素的值，因此我们将只需要在缺失时归因于一个变量。通过一个灵活的模型，该指数将被包括在流行病学分析中，我们将能够提供X和Y之间的因果联系的直接估计，因为所有混杂因素都已被考虑在内。我们将首先基于个人水平的数据建立我们的框架，然后将其扩展到聚集水平，例如，通常用于总结流行病风险的空间和时空变化的小区域研究(例如，用于疾病监测)或关注病因学问题(例如，揭示环境/社会决定死亡或发病率的因素)。我们将使用贝叶斯全概率建模，它提供了一种灵活的方法，纳入了关于缺失数据机制的不同假设，并适应了不同的缺失数据模式，通过现实的模拟研究，我们将评估该框架的特性，并将其与其他最先进的方法进行比较。此外，还将考虑两个真实的案例研究。第一个将评估在英格兰北部水中暴露于氯的情况下低出生体重的风险，并将基于个人水平数据。第二项调查将调查空气污染浓度和噪音暴露对英格兰和威尔士因心血管原因入院的影响，并将在小范围内进行。通过案例研究，我们将能够揭示，与仅基于人口登记数据的常用分析相比，我们提出的方法如何在暴露对健康结果的影响方面改变流行病学分析的结果。这将有可能转化为卫生政策和战略的变化，以考虑到改进后的、更准确的结果，并可能成为分析观察性研究的新的最先进的方法。

项目成果

Using ecological propensity score to adjust for missing confounders in small area studies.

• DOI: 10.1093/biostatistics/kxx058
• 发表时间: 2019-01-01
• 期刊: Biostatistics (Oxford, England)
• 作者: Wang Y;Pirani M;Hansell AL;Richardson S;Blangiardo M
• 通讯作者: Blangiardo M

A flexible hierarchical framework for improving inference in area-referenced environmental health studies.

• DOI: 10.1002/bimj.201900241
• 发表时间: 2020-11
• 期刊: Biometrical journal. Biometrische Zeitschrift
• 作者: Pirani M;Mason AJ;Hansell AL;Richardson S;Blangiardo M
• 通讯作者: Blangiardo M

A joint Bayesian space-time model to integrate spatially misaligned air pollution data in R-INLA

• DOI: 10.1002/env.2644
• 发表时间: 2020-07-29
• 期刊: ENVIRONMETRICS
• 影响因子: 1.7
• 作者: Forlani, C.;Bhatt, S.;Blangiardo, M.
• 通讯作者: Blangiardo, M.

Using Ecological Propensity Score to Adjust for Missing Confounders in Small Area Studies

使用生态倾向评分来调整小区域研究中缺失的混杂因素

• DOI: 10.48550/arxiv.1605.00814
• 发表时间: 2016
• 作者: Wang Y