e-journal

Spatial health effects analysis with uncertain residential locations

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Spatial epidemiology has benefited greatly from advances in geographic information system technology,
which permits extensive study of associations between various health responses and a wide array of socioeconomic and environmental factors. However, many spatial epidemiological datasets have missing values
for a substantial proportion of spatial variables, such as the census tract of residence of study participants.
The standard approach is to discard these observations and analyze only complete observations. In this
article, we propose a new hierarchical Bayesian spatial model to handle missing observation locations. Our
model utilizes all available information to learn about the missing locations and propagates uncertainty
about the missing locations throughout the model. We show via a simulation study that this method can
lead to more efficient epidemiological analysis. The method is applied to a study of the relationship
between fine particulate matter and birth outcomes is southeast Georgia, where we find smaller
posterior variance for most parameters using our missing data model compared to the standard
complete case model.

Keywords
Bayesian hierarchical model, conditionally autoregressive prior, data imputation, geographic information
system, missing data

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Informasi Detail

Judul Seri

Statistical Methods in Medical Research

No. Panggil

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Penerbit

London : SAGE., 2014

Deskripsi Fisik

Statistical Methods in Medical Research 2014, Vol. 23(2) 156–168

Bahasa

English

ISBN/ISSN

DOI: 10.1177/0962280

Klasifikasi

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Tipe Isi

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Tipe Media

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Tipe Pembawa

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Edisi

2014, Vol. 23(2)

Subjek

STATISTIK

Info Detail Spesifik

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Pernyataan Tanggungjawab

agus

Versi lain/terkait

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