Non-parametric estimation of spatial variation in relative risk

J. E. Kelsall, P. J. Diggle

Research output: Contribution to journalArticle

Abstract

We consider the problem of estimating the spatial variation in relative risks of two diseases, say, over a geographical region. Using an underlying Poisson point process model, we approach the problem as one of density ratio estimation implemented with a non-parametric kernel smoothing method. In order to assess the significance of any local peaks or troughs in the estimated risk surface, we introduce pointwise tolerance contours which can enhance a greyscale image plot of the estimate. We also propose a Monte Carlo test of the null hypothesis of constant risk over the whole region, to avoid possible over-interpretation of the estimated risk surface. We illustrate the capabilities of the methodology with two epidemiological examples.

Original languageEnglish (US)
Pages (from-to)2335-2342
Number of pages8
JournalStatistics in Medicine
Volume14
Issue number21-22
StatePublished - 1995
Externally publishedYes

Fingerprint

Relative Risk
Nonparametric Estimation
Monte Carlo Test
Nonparametric Smoothing
Kernel Smoothing
Poisson Point Process
Smoothing Methods
Kernel Methods
Null hypothesis
Process Model
Tolerance
Methodology
Estimate

ASJC Scopus subject areas

  • Epidemiology

Cite this

Kelsall, J. E., & Diggle, P. J. (1995). Non-parametric estimation of spatial variation in relative risk. Statistics in Medicine, 14(21-22), 2335-2342.

Non-parametric estimation of spatial variation in relative risk. / Kelsall, J. E.; Diggle, P. J.

In: Statistics in Medicine, Vol. 14, No. 21-22, 1995, p. 2335-2342.

Research output: Contribution to journalArticle

Kelsall, JE & Diggle, PJ 1995, 'Non-parametric estimation of spatial variation in relative risk', Statistics in Medicine, vol. 14, no. 21-22, pp. 2335-2342.
Kelsall, J. E. ; Diggle, P. J. / Non-parametric estimation of spatial variation in relative risk. In: Statistics in Medicine. 1995 ; Vol. 14, No. 21-22. pp. 2335-2342.
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