Search PubMedSearch

PubMed · 3764224

Calculating expected mortality.

Abstract

The widely used 'person-years method' of calculating expected mortality has been discussed recently by several authors. In studies where mortality is either lower or higher than the standard mortality of some reference population, the use of exposure to death as an estimator of the expected number of deaths will generally lead to bias, always exaggerating the difference between study and standard mortality. This bias is examined in a proportional hazards model. The recent suggestion by Hartz et al.1 of calculating the mortalities of individuals during their 'potential follow-up time' is claimed to be only rarely feasible in practice.

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

N Keiding, M Vaeth. Calculating expected mortality.. https://doi.org/10.1002/sim.4780050405

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

KEEP EXPLORING

Related citations

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