Biomedical subjects

L Knorr-Held

Publications and source records attributed to L Knorr-Held.

6 recordsLinked to original sources

PubMed2002

A Bayesian model for spatial wildlife disease prevalence data.

The analysis of the geographical distribution of disease on the scale of geographic areas such as administrative boundaries plays an important role in veterinary epidemiology. Prevalence estimates of wildlife population surveys are often based on regional count data generated by sampling animals shot by hunters. The observed disease rate per spatial unit is not an useful estimate of the underlying disease prevalence due to different sample sizes and spatial dependencies between neighbouring areas. Therefore, it is necessary to account for extra-sample variation and spatial correlations in the data to produce more accurate maps of disease incidence. The detection of spatial patterns is complicated by missing data in many of the geographical areas as the complete coverage of all areas is nearly impossible in wildlife surveys. For this purpose a hierarchical Bayesian model in which structured and unstructured over dispersion is modelled explicitly in terms of spatial and non-spatial components was implemented by Markov chain Monte Carlo methods. The model was empirically compared with the results of a non-spatial beta-binomial model using surveillance data of pseudorabies virus infections of European wild boars (Sus scrofa scrofa L.) in the Federal State of Brandenburg, Germany.

Animal Diseases↗

PubMed2001

Projections of lung cancer mortality in West Germany: a case study in Bayesian prediction.

We apply a generalized Bayesian age-period-cohort (APC) model to a data-set on lung cancer mortality in West Germany, in the period 1952-1996. Our goal is to predict future death rates until the year 2010, separately for males and females. Since age and period are not measured on the same grid, we propose a generalized APC model where consecutive cohort parameters represent strongly overlapping birth cohorts. This approach results in a rather large number of parameters, where standard algorithms for statistical inference by Markov chain Monte Carlo methods turn out to be computationally intensive. We propose a more efficient implementation based on ideas of block sampling from the time series literature. We entertain two different formulations, penalizing either first or second differences of age, period and cohort parameters. To assess the predictive quality of both formulations, we first forecast the rates for the period 1987-1996 based on data until 1986. A comparison with the actual observed rates is made based on a predictive deviance criterion. Predictions of lung cancer mortality until 2010 are then reported and a modification of the formulation in order to include information on cigarette consumption is finally described.To whom correspondence should be addressed. Currently at Imperial College School of Medicine, Department of Epidemiology and Public Health, Norfolk Place, London W2 1PG, UK.

Journal Article↗

PubMed2000

Bayesian detection of clusters and discontinuities in disease maps.

An interesting epidemiological problem is the analysis of geographical variation in rates of disease incidence or mortality. One goal of such an analysis is to detect clusters of elevated (or lowered) risk in order to identify unknown risk factors regarding the disease. We propose a nonparametric Bayesian approach for the detection of such clusters based on Green's (1995, Biometrika 82, 711-732) reversible jump MCMC methodology. The prior model assumes that geographical regions can be combined in clusters with constant relative risk within a cluster. The number of clusters, the location of the clusters, and the risk within each cluster is unknown. This specification can be seen as a change-point problem of variable dimension in irregular, discrete space. We illustrate our method through an analysis of oral cavity cancer mortality rates in Germany and compare the results with those obtained by the commonly used Bayesian disease mapping method of Besag, York, and Mollié (1991, Annals of the Institute of Statistical Mathematics, 43, 1-59).

Bayes Theorem↗

PubMed1998

Modelling risk from a disease in time and space.

This paper combines existing models for longitudinal and spatial data in a hierarchical Bayesian framework, with particular emphasis on the role of time- and space-varying covariate effects. Data analysis is implemented via Markov chain Monte Carlo methods. The methodology is illustrated by a tentative re-analysis of Ohio lung cancer data 1968-1988. Two approaches that adjust for unmeasured spatial covariates, particularly tobacco consumption, are described. The first includes random effects in the model to account for unobserved heterogeneity; the second adds a simple urbanization measure as a surrogate for smoking behaviour. The Ohio data set has been of particular interest because of the suggestion that a nuclear facility in the southwest of the state may have caused increased levels of lung cancer there. However, we contend here that the data are inadequate for a proper investigation of this issue.

Adolescent↗

PubMedDate not supplied

Bayesian modelling of inseparable space-time variation in disease risk.

This paper proposes a unified framework for a Bayesian analysis of incidence or mortality data in space and time. We introduce four different types of prior distributions for space x time interaction in extension of a model with only main effects. Each type implies a certain degree of prior dependence for the interaction parameters, and corresponds to the product of one of the two spatial with one of the two temporal main effects. The methodology is illustrated by an analysis of Ohio lung cancer data 1968-1988 via Markov chain Monte Carlo simulation. We compare the fit and the complexity of several models with different types of interaction by means of quantities related to the posterior deviance. Our results confirm an epidemiological hypothesis about the temporal development of the association between urbanization and risk factors for cancer.

Bayes Theorem↗

PubMedDate not supplied

Modelling categorical covariates in Bayesian disease mapping by partition structures.

We consider the problem of mapping the risk from a disease using a series of regional counts of observed and expected cases, and information on potential risk factors. To analyse this problem from a Bayesian viewpoint, we propose a methodology which extends a spatial partition model by including categorical covariate information. Such an extension allows detection of clusters in the residual variation, reflecting further, possibly unobserved, covariates. The methodology is implemented by means of reversible jump Markov chain Monte Carlo sampling. An application is presented in order to illustrate and compare our proposed extensions with a purely spatial partition model. Here we analyse a well-known data set on lip cancer incidence in Scotland.

Bayes Theorem↗