Estimation of spatial variation in risk using matched case-control data

Mikala F. Jarner, Peter J. Diggle, Amanda G. Chetwynd

Research output: Contribution to journalArticle

Abstract

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.

Original languageEnglish (US)
Pages (from-to)936-945
Number of pages10
JournalBiometrical Journal
Volume44
Issue number8
DOIs
StatePublished - 2002
Externally publishedYes

Fingerprint

Case-control Data
Environmental Epidemiology
Matched Case-control Study
Generalized Additive Models
Risk Factors
Estimate
Spatial variation
Model
Context
Epidemiology
Risk factors
Generalized additive models
England

Keywords

  • Epidemiology
  • Generalised additive models
  • Matched case-control design
  • Spatial variation in risk

ASJC Scopus subject areas

  • Statistics and Probability

Cite this

Estimation of spatial variation in risk using matched case-control data. / Jarner, Mikala F.; Diggle, Peter J.; Chetwynd, Amanda G.

In: Biometrical Journal, Vol. 44, No. 8, 2002, p. 936-945.

Research output: Contribution to journalArticle

Jarner, Mikala F. ; Diggle, Peter J. ; Chetwynd, Amanda G. / Estimation of spatial variation in risk using matched case-control data. In: Biometrical Journal. 2002 ; Vol. 44, No. 8. pp. 936-945.
@article{c85d4764d3a049c9a5c90d3d66163c39,
title = "Estimation of spatial variation in risk using matched case-control data",
abstract = "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.",
keywords = "Epidemiology, Generalised additive models, Matched case-control design, Spatial variation in risk",
author = "Jarner, {Mikala F.} and Diggle, {Peter J.} and Chetwynd, {Amanda G.}",
year = "2002",
doi = "10.1002/bimj.200290005",
language = "English (US)",
volume = "44",
pages = "936--945",
journal = "Biometrical Journal",
issn = "0323-3847",
publisher = "Wiley-VCH Verlag",
number = "8",

}

TY - JOUR

T1 - Estimation of spatial variation in risk using matched case-control data

AU - Jarner, Mikala F.

AU - Diggle, Peter J.

AU - Chetwynd, Amanda G.

PY - 2002

Y1 - 2002

N2 - 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.

AB - 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.

KW - Epidemiology

KW - Generalised additive models

KW - Matched case-control design

KW - Spatial variation in risk

UR - http://www.scopus.com/inward/record.url?scp=0036921398&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=0036921398&partnerID=8YFLogxK

U2 - 10.1002/bimj.200290005

DO - 10.1002/bimj.200290005

M3 - Article

VL - 44

SP - 936

EP - 945

JO - Biometrical Journal

JF - Biometrical Journal

SN - 0323-3847

IS - 8

ER -