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Analysis of aberrations in public health surveillance data: Estimating variances on correlated samples
Author(s) -
Kafadar Karen,
Stroup Donna F.
Publication year - 1992
Publication title -
statistics in medicine
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.996
H-Index - 183
eISSN - 1097-0258
pISSN - 0277-6715
DOI - 10.1002/sim.4780111203
Subject(s) - jackknife resampling , variance (accounting) , statistics , correlation , econometrics , sample (material) , identification (biology) , sample size determination , computer science , mathematics , chemistry , geometry , accounting , chromatography , estimator , business , botany , biology
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.