Corpus ID: 16957811

MIXTURES OF FACTOR MODELS FOR MULTIVARIATE DISEASE RATES

@inproceedings{Bailey2011MIXTURESOF,
  title={MIXTURES OF FACTOR MODELS FOR MULTIVARIATE DISEASE RATES},
  author={T. Bailey},
  year={2011}
}
• A range of different approaches have been suggested for the multivariate modelling of the geographical distribution of different but potentially related diseases. We suggest an addition to these methods which incorporates a discrete mixture of latent factors, as opposed to using CAR or MCAR random effect formulations. Our proposal provides for a potentially richer range of dependency structures than those encompassed in previously used models in that it is capable of representing an enhanced… Expand

Figures and Tables from this paper

Multivariate Bayesian meta-analysis: joint modelling of multiple cancer types using summary statistics
TLDR
A multivariate Bayesian meta-analysis model, which can model multiple cancers jointly using summary measures without requiring access to the unit record data, is proposed, which could be useful whenunit record data are unavailable because of privacy and confidentiality requirements. Expand
Augmenting disease maps: a Bayesian meta-analysis approach
TLDR
A hierarchical Bayesian meta-analysis model is proposed that analyses the point and interval estimates from an online atlas that aims to reveal patterns of cancer incidence for the 20 cancers included in ACA in major cities, regional and remote areas. Expand

References

SHOWING 1-10 OF 40 REFERENCES
Modelling multivariate disease rates with a latent structure mixture model
TLDR
This paper develops a model which incorporates a discrete mixture of latent structures and argues that this provides potential to represent an enhanced range of correlation structures between diseases at the same time as implicitly allowing for less restrictive spatialrelation structures between geographical units. Expand
Towards joint disease mapping
TLDR
A review of methods for separate analyses of diseases, then a move to ecological regression approaches, where the rates from one of the diseases enter as surrogate covariates for exposure, and a general framework for jointly modelling the variation of two or more diseases. Expand
A shared component model for detecting joint and selective clustering of two diseases
TLDR
A shared component model is proposed for the joint spatial analysis of two diseases to separate the underlying risk surface for each disease into a shared and a disease-specific component. Expand
Issues in the mapping of two diseases
TLDR
A proportional mortality approach is proposed to give clues to areas of similarity and dissimilarity between diseases in order to provide better estimates of risk in each area. Expand
Loglinear spatial factor analysis: an application to diabetes mellitus complications
TLDR
The methodology proposed can be seen as an extension of the geostatistical linear model of coregionalization, and of the related 'factorial kriging analysis', to the case of geo-referenced, in general multi-way, contingency tables. Expand
Empirical Bayes estimates of age-standardized relative risks for use in disease mapping.
TLDR
A new approach using empirical Bayes estimation is proposed to map incidence and mortality from diseases such as cancer and the resulting estimators represent a weighted compromise between the SMR, the overall mean relative rate, and a local mean of the relative rate in nearby areas. Expand
Polytomous disease mapping to detect uncommon risk factors for related diseases.
  • E. Dreassi
  • Medicine
  • Biometrical journal. Biometrische Zeitschrift
  • 2007
TLDR
It turns out that oral cavity and larynx cancer have different spatial patterns for residual risk factors which are not the typical ones such as smoking habits and alcohol consumption. Expand
Generalized spatial structural equation models.
TLDR
A generalized spatial structural equation model is proposed that is applied to county-level cancer mortality and census summary data for Minnesota, including socioeconomic status and access to public utilities, and used to model the relationship among the underlying factors. Expand
Generalized common spatial factor model.
TLDR
The model is applied to county-level cancer mortality data in Minnesota to find whether there exists a common spatial factor underlying the cancer mortality throughout the state. Expand
Accounting for inaccuracies in population counts and case registration in cancer mapping studies
TLDR
This paper proposes several approaches for modelling population counts and investigates the sensitivity of inference to the sizes of errors in health and population data, and illustrates the methods proposed using data for breast cancer in the Thames region of the UK. Expand
...
1
2
3
4
...