Breast cancer-susceptibility genes BRCA1 and BRCA2 have recently been identified on the human genome. Women who carry a mutation of one of these genes have a greatly increased chance of developing breast and ovarian cancer, and they usually develop the disease at a much younger age, compared with normal individuals. Women can be tested to see whether they are carriers. A woman who undergoes genetic counseling before testing can be told the probabilities that she is a carrier, given her family history. In this paper we develop a model for evaluating the probabilities that a woman is a carrier of a mutation of BRCA1 and BRCA2, on the basis of her family history of breast and ovarian cancer in first- and second-degree relatives. Of special importance are the relationships of the family members with cancer, the ages at onset of the diseases, and the ages of family members who do not have the diseases. This information can be elicited during genetic counseling and prior to genetic testing. The carrier probabilities are obtained from Bayes's rule, by use of family history as the evidence and by use of the mutation prevalences as the prior distribution. In addressing an individual's carrier probabilities, we incorporate uncertainty about some of the key inputs of the model, such as the age-specific incidence of diseases and the overall prevalence of mutations. There is some evidence that other, undiscovered genes may be important in explaining familial breast cancer. Users of the current version of the model should be aware of this limitation. The methodology that we describe can be extended to more than two genes, should data become available about other genes.
We propose a novel framework for estimating the time-varying covariation among stocks. Our work is inspired by asset pricing theory and associated developments in Financial Index Models. We work with a family of highly structured dynamic factor models that seek the extraction of the latent structure responsible for the cross-sectional covariation in a large set of financial securities. Our models incorporate stock specific information in the estimation of commonalities and deliver economically interpretable factors that are used both, as a vehicle to estimate large time-varying covariance matrix, and as a potential tool for stock selection in portfolio allocation problems. In an empirically oriented, high-dimensional case study, we showcase the use of our methodology and highlight the flexibility and power of the dynamic factor model framework in financial econometrics.
The VA management services department i n vests considerably in the collection and assessment of data to inform on hospital and care-area speci c levels of quality of care. Resulting time series of quality monitors provide information relevant t o e v aluating patterns of variability in hospital-speci c quality of care over time and across care areas, and to compare and assess di erences across hospitals. In collaboration with the VA management services group we h a ve developed various models for evaluating such patterns of dependencies and combining data across the VA hospital system. This paper provides a brief overview of resulting models, some summary examples on three monitor time series, and discussion of data, modelling and inference issues. This work introduces new models for multivariate nonGaussian time series. The framework combines cross-sectional, hierarchical models of the population of hospitals with time series structure to allow a n d measure time-variations in the associated hierarchical model parameters. In the VA study, the within-year components of the models describe patterns of heterogeneity across the population of hospitals and relationships among several such monitors, while the time series components describe patterns of variability through time in hospital-speci c e ects and their relationships across quality monitors. Additional model components isolate unpredictable aspects of variability i n q u a l i t y monitor outcomes, by h o spital and care areas. We discuss model assessment, residual analysis and MCMC algorithms developed to t these models, which w i l l b e o f i n terest in related applications in other socio-economic areas.
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