Biomedical subjects

Mary M Louie

Publications and source records attributed to Mary M Louie.

3 recordsLinked to original sources

PubMed2006

A multiscale method for disease mapping in spatial epidemiology.

The effects of spatial scale in disease mapping are well-recognized, in that the information conveyed by such maps varies with scale. Here we provide an inferential framework, in the context of tract count data, for describing the distribution of relative risk simultaneously across a hierarchy of multiple scales. In particular, we offer a multiscale extension of the canonical standardized mortality ratio (SMR), consisting of Bayesian posterior-based strategies for both estimation and characterization of uncertainty. As a result, a hierarchy of informative disease and confidence maps can be produced, without the need to first try to identify a single appropriate scale of analysis. We explore the behaviour of the proposed methodology in a small simulation study, and we illustrate its usage through an application to data on gastric cancer in Tuscany.

Bayes Theorem↗

PubMed2006

Multiscale detection of localized anomalous structure in aggregate disease incidence data.

We present a modelling framework for detection of potentially anomalous structure in aggregate spatial disease incidence data in a manner sensitive to localization at multiple scales and/or positions. The key technical contribution is the re-casting of the components of a multiscale disease mapping methodology, recently introduced by the authors in an earlier paper, into a form appropriate for hypothesis testing. In particular, we describe how hypotheses of spatially clustered variations in disease incidence may be linked in one-to-one correspondence with collections of hypotheses on the values of certain multiscale parameters associated with a user-defined hierarchy of nested partitions of an overall spatial region. A Bayesian hypothesis testing methodology is developed in the context of a standard Poisson measurement model, over the collection of possible multiscale hypotheses. We discuss the specification of hyper parameters and prior distributions on the space of models. The methodology is illustrated on both simulated and real data.

Cluster Analysis↗

PubMed2003

Breast cancer risk prediction with a log-incidence model: evaluation of accuracy.

OBJECTIVE: We examined whether a breast cancer risk prediction model other than the Gail et al. model performs better at discriminating between women who will and who will not develop the disease. METHODS: We applied the two published versions of the Rosner and Colditz log-incidence model of breast cancer, developed on data from the Nurses' Health Study, to the estimation of 5-year risk for the period 1992 to 1997 in the same cohort. The first version contained reproductive factors only, and the second version contained a more extensive list of risk factors. RESULTS: Both versions of the model fit well. The ratio of expected to observed numbers of cases (E/O) in the first version was 1.00 (95% confidence interval [CI] 0.93-1.07); for the extended version the E/O was 1.01 (95% CI 0.94-1.09). The age-adjusted concordance statistic was 0.57 for the first model version and 0.63 for the extended version. CONCLUSION: The discriminatory accuracy of the two versions was modest, although the addition of the variables in the extended version meaningfully increased the discriminatory accuracy of risk prediction over that found with the more parsimonious model.

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