Non-parametric estimation of spatial variation in relative risk

Stat Med. 1995 Nov 15-30;14(21-22):2335-42. doi: 10.1002/sim.4780142106.


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.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Air Pollution / adverse effects
  • Bias
  • Cluster Analysis*
  • England / epidemiology
  • Female
  • Humans
  • Infant, Newborn
  • Laryngeal Neoplasms / epidemiology
  • Lung Neoplasms / epidemiology
  • Male
  • Models, Statistical*
  • Monte Carlo Method
  • Poisson Distribution
  • Population Density
  • Reproducibility of Results
  • Risk Assessment
  • Risk*
  • Sex Ratio
  • Space-Time Clustering
  • Statistics, Nonparametric*