PubMed Health⌕ Search
Home/Archive/PubMed

PubMed · 998608

Stratification by a multivariate confounder score.

O S Miettinen
View original source ↗Export metadata ↓

Abstract

The complexity and inefficiency of multiple cross-classification as a means of controlling confounding in etiologic research may be avoided upon summarizing the pattern of confounding factors for each subject in terms of a multivariate score. The control of confounding may be based on stratification by the score, with stratum-specific contingency tables obtained and analyzed in the usual manner.

Explore related subjects

BiometryEpidemiologic MethodsHumansJournal ArticleResearch Support, U.S. Gov't, P.H.S.

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

O S Miettinen. 1976. Stratification by a multivariate confounder score.. https://doi.org/10.1093/oxfordjournals.aje.a112339

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related citations

PubMed1992

Coloured noise or low-dimensional chaos?

Devising a method capable of distinguishing a low-dimensional chaotic signal that might be embedded in a noisy stochastic process has become a major challenge for those involved in time-series analysis. Here a null hypothesis approach is used in conjunction with a known nonlinear predictive test, to probe for the presence of chaos in epidemiological data. A probabilistic set of rules is used to stimulate a historic record of New York City measles outbreaks, generally understood to be governed by a chaotic attractor. The simulated runs of 'surrogate data' are carefully constructed so as to be free from any underlying low-dimensional chaotic process. They therefore serve as a useful null model against which to test the observed time series. However, despite the assumed differences between the dynamics of measles outbreaks and the null model, a nonlinear predictive scheme is found to be unable to differentiate between their characteristic time series. The methodology confirms that, if there is in fact a chaotic signal in the measles data, it is extremely difficult to detect in time series of such limited length. The results have general relevance to the analysis of physical, ecological and environmental time series.

Biometry↗