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危险因素/暴露X对健康结果y的影响总是存在混淆的问题。队列研究是理想的信息来源，因为它们通常包含一组丰富的个人级变量。然而，仅基于队列的研究可能会遭受选择偏见问题和人口代表性缺乏的问题。队列研究还可能缺乏评估罕见结果的统计能力，并且地理或其他群体级别的变化限制了可以研究诸如区域水平社会剥夺之类的上下文因素的程度。就代表性而言，经常收集的行政数据是一个很好的选择。但是，这些数据源通常对大量人群的变量数量有限，并且可能会错过重要的预测因素/混杂因素，从而导致对风险的有偏见的估计。我们提出了一个一般框架，该框架整合了这两个数据源，利用了来自同类群体/人群的统计权力和人群代表性的详细信息。该策略需要将丢失的数据归纳为行政数据集，其中包含目标人群中每个人的数据，而人群/调查通常仅涵盖一个个体的一个子集，因此从后一个来源获得的混杂因素将被部分测量（即，对于注册机构中的某些单位而言，将缺少。考虑到可能要考虑的可能大量的混杂因素，每个单一混杂因素可以证明计算上的不可行，并将其限制在几个假设上。我们将建立一个倾向得分，例如索引（我们称之为部分倾向得分-PPS -pps），总结了同类/监视的混杂因子值，因此我们只需要一个丢失时就需要一个变量。通过灵活的模型，指数将包括在流行病学分析中，我们将能够直接估计X和Y之间的因果关系，因为所有混杂因素已被考虑在内。我们将首先在单个级别的数据上构建我们的框架，然后将其扩展到汇总级别，例如。小区域研究通常用于总结流行病学风险的时空和时空变化（例如，用于疾病监测）或专注于病因问题（例如，揭示了死亡率或发病率的环境/社会决定因素）。我们将使用贝叶斯的完全概率建模，该模型提供了一种灵活的方法，可以将有关丢失的数据机制的不同假设结合起来，并适应丢失的数据的不同模式，通过逼真的仿真研究，我们将评估框架的属性，并将其与其他最先进的方法进行比较。另外，将考虑两个实际案例研究。第一个将评估英格兰北部水中接触氯的低出生体重风险，并将基于个人级别的数据。第二个将调查空气污染浓度和噪声暴露对英格兰和威尔士心血管原因的住院的影响，并将处于小面积。通过案例研究，与仅基于人口注册表的数据相比，与常用的分析相比，我们提出的方法如何根据暴露对健康结果的影响来改变流行病学分析的结果。这将有可能转化为健康政策和策略的变化，以考虑改进，更准确的结果，并可能成为分析观察性研究的最新方法。

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