Use this URL to cite or link to this record in EThOS: http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.724115
Title: Using propensity score to adjust for unmeasured confounders in small area studies of environmental exposures and health
Author: Wang, Yingbo
Awarding Body: Imperial College London
Current Institution: Imperial College London
Date of Award: 2015
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Abstract:
Small area studies are commonly used in epidemiology to assess the impact of risk factors on health outcomes when data are available at the aggregated level. However the estimates are often biased due to unmeasured confounders which cannot be taken into account. Integrating individual-level information into area-level data in ecological studies may help reduce bias. To investigate this, I develop an area/ecological level propensity score (PS) to integrate individual-level data and then synthesise the area-level PS with routinely available area-level datasets, such as hospital episode statistics (HES) and census data. This framework comprises three steps: 1. Individual level survey data is used to obtain information on the potential confounders, which are not measured at the area-level. Using a Bayesian hierarchical framework I synthesise these variables and calculate PS at the ecological level, taking into the account the correlation among the potential confounders. 2. The calculated PS is included as a scalar quantity in the regression model linking environmental exposure/risk factors and health outcome. As PS has no epidemiological interpretation, I introduce a number of flexible functions to allow for nonlinear effects, such as fixed-knot splines, reversible jump MCMC (RJ) and random walk (RW). 3. As real surveys are typically characterized by a limited coverage compared to small area studies, I impute the ecological PS in the areas with no survey coverage. I propose two new imputation models: random walk and cluster imputation (including a) regression tree and b) profile regression ) to relax the assumption of linearity, and through simulations, both imputation models are proven to produce better results than the traditional linear imputation model. I conclude that integrating individual-level data via PS is a promising method to reduce the bias intrinsic in ecological studies due to unmeasured confounders and I introduce a real application on small area studies for evaluating the effect of air pollution on CVD hospital admissions in England.
Supervisor: Blangiardo, Marta ; Best, Nicky ; Richardson, Sylvia Sponsor: Medical Research Council
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID: uk.bl.ethos.724115  DOI: Not available
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