Estimation of Spatial Variation in Risk Using Matched Case-control Data

A common problem in environmental epidemiology is to estimate spatial variation in disease risk after accounting for known risk factors. In this paper we consider this problem in the context of matched case‐control studies. We extend the generalised additive model approach of Kelsall and Diggle (199...

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Bibliographic Details
Published in:Biometrical journal 2002-12, Vol.44 (8), p.936-945
Main Authors: Jarner, Mikala F., Diggle, Peter, Chetwynd, Amanda G.
Format: Article
Language:English
Subjects:
Applications
Data processing
Epidemiology
Exact sciences and technology
Generalised additive models
Inference from stochastic processes
time series analysis
Matched case-control design
Mathematics
Medical sciences
Multivariate analysis
Probability and statistics
Risk factors
Sciences and techniques of general use
Spatial variation in risk
spatial variations
Statistics
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Summary:A common problem in environmental epidemiology is to estimate spatial variation in disease risk after accounting for known risk factors. In this paper we consider this problem in the context of matched case‐control studies. We extend the generalised additive model approach of Kelsall and Diggle (1998) to studies in which each case has been individually matched to a set of controls. We discuss a method for fitting this model to data, apply the method to a matched study on perinatal death in the North West Thames region of England and explain why, if spatial variation is of particular scientific interest, matching is undesirable.
ISSN:0323-3847
1521-4036
DOI:10.1002/bimj.200290005