Analysis of aberrations in public health surveillance data: Estimating variances on correlated samples
Article first published online: 12 OCT 2006
Copyright © 1992 John Wiley & Sons, Ltd.
Statistics in Medicine
Volume 11, Issue 12, pages 1551–1568, 1992
How to Cite
Kafadar, K. and Stroup, D. F. (1992), Analysis of aberrations in public health surveillance data: Estimating variances on correlated samples. Statist. Med., 11: 1551–1568. doi: 10.1002/sim.4780111203
- Issue published online: 12 OCT 2006
- Article first published online: 12 OCT 2006
- Manuscript Revised: APR 1992
The detection of unusual patterns in health data presents an important challenge to health workers interested in early identification of epidemics or important risk factors. A useful procedure for detection of aberrations is the ratio of a current report to some historic baseline. This work addresses the problem of finding the variance of such a ratio when the surveillance reports are correlated. Results show that, when estimating this variance or the variance of the sample mean from a series of observations with an estimated correlation structure, bootstrap and jackknife estimates may be overly optimistic. The delta method or a classical method may be more useful when such model dependence is inappropriate.