Using ecological propensity score to adjust for missing confounders in small area studies
File(s)
Published version
Author(s)
Wang, Y
Pirani, M
Hansell, A
Richardson, S
Blangiardo, MAG
Type
Journal Article
Abstract
Small area ecological studies are commonly used in epidemiology to assess the impact of area level risk factors on health outcomes when data are only available in an aggregated form. However, the resulting estimates are often biased due to unmeasured confounders, which typically are not available from the standard administrative registries used for these studies. Extra information on confounders can be provided through external data sets such as surveys or cohorts, where the data are available at the individual level rather than at the area level; however, such data typically lack the geographical coverage of administrative registries. We develop a framework of analysis which combines ecological and individual level data from different sources to provide an adjusted estimate of area level risk factors which is less biased. Our method (i) summarizes all available individual level confounders into an area level scalar variable, which we call ecological propensity score (EPS), (ii) implements a hierarchical structured approach to impute the values of EPS whenever they are missing, and (iii) includes the estimated and imputed EPS into the ecological regression linking the risk factors to the health outcome. Through a simulation study, we show that integrating individual level data into small area analyses via EPS is a promising method to reduce the bias intrinsic in ecological studies due to unmeasured confounders; we also apply the method to a real case study to evaluate the effect of air pollution on coronary heart disease hospital admissions in Greater London.
Date Issued
2019-01
Date Acceptance
2017-10-13
Citation
Biostatistics, 2019, 20 (1), pp.1-16
ISSN
1465-4644
Publisher
Oxford University Press (OUP)
Start Page
1
End Page
16
Journal / Book Title
Biostatistics
Volume
20
Issue
1
Copyright Statement
© The Author 2017. Published by Oxford University Press. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited.
Sponsor
Medical Research Council
Medical Research Council (MRC)
Medical Research Council (MRC)
Medical Research Council (MRC)
Grant Number
MR/M025195/1
G0801056
MR/L01341X/1
MR/M501669/1
Subjects
Science & Technology
Life Sciences & Biomedicine
Physical Sciences
Mathematical & Computational Biology
Statistics & Probability
Mathematics
Environmental epidemiology
Hierarchical model
Missing data
Observational study
Propensity score
Spatial statistics
LONG-TERM EXPOSURE
AIR-POLLUTION
MULTIPLE-IMPUTATION
REGRESSION-MODELS
VALIDATION DATA
Air Pollution
Biostatistics
Computer Simulation
Coronary Disease
Data Interpretation, Statistical
Epidemiologic Methods
Humans
London
Patient Admission
Propensity Score
Small-Area Analysis
Humans
Coronary Disease
Patient Admission
Epidemiologic Methods
Small-Area Analysis
Data Interpretation, Statistical
Air Pollution
Computer Simulation
London
Biostatistics
Propensity Score
Statistics & Probability
0104 Statistics
0604 Genetics
Publication Status
Published
Date Publish Online
2017-11-09