Commonly used methods for depicting geographic Variation in cancer rates ar
e based on rankings. They identify where the rates are high and low but do
not indicate the magnitude of the rates nor their variability. Yet such mea
sures of variability may be useful in suggesting which types of cancer warr
ant further analytic studies of localized risk factors. We consider a mixed
effects model in which the logarithm of the mean Poisson rate is additive
in fixed stratum effects (e.g., age effects) and in logarithms of random re
lative risk effects associated with geographic areas. These random effects
are assumed to follow a gamma distribution with unit mean and variance 1/al
pha, similar to Clayton and Kaldor (1987, Biometrics 43, 671-681). We prese
nt maximum likelihood and method-of-moments estimates with standard errors
for inference on alpha(-1/2), the relative risk standard deviation (RRSD).
The moment estimates rely an only the first two moments of the Poisson and
gamma distributions but have larger standard errors than the maximum likeli
hood estimates. We compare these estimates with other measures of variabili
ty. Several examples suggest that the RRSD estimates have advantages compar
ed to other measures of variability.