Articles: disease.
-
Statistics in medicine · Apr 2009
Modeling disease-state transition heterogeneity through Bayesian variable selection.
In many diseases, Markov transition models are useful in describing transitions between discrete disease states. Often the probability of transitioning from one state to another varies widely across subjects. This heterogeneity is driven, in part, by a possibly unknown number of previous disease states and by potentially complex relationships between clinical data and these states. ⋯ Our approach simultaneously estimates the order of the Markov process and the transition-specific covariate effects. The methods are assessed using simulation studies and applied to model disease-state transition on the expanded disability status scale (EDSS) in multiple sclerosis (MS) patients from the Partners MS Center in Boston, MA. The proposed methodology is shown to accurately identify complex covariate-transition relationships in simulations and identifies a clinically significant interaction between relapse history and EDSS history in MS patients.
-
Disparities in health status between Aboriginal and Torres Strait Islander peoples and the total Australian population have been documented in a fragmentary manner using disparate health outcome measures. ⋯ Comprehensive information on the burden of disease for Indigenous Australians is essential for informed health priority setting. This assessment has identified large health gaps which translate into opportunities for large health gains. It provides the empirical base to determine a more equitable and efficient funding of Indigenous health in Australia. The methods are replicable and would benefit priority setting in other countries with great disparities in health experienced by Indigenous peoples or other disadvantaged population groups.
-
Making sense of rapidly evolving evidence on genetic associations is crucial to making genuine advances in human genomics and the eventual integration of this information in the practice of medicine and public health. Assessment of the strengths and weaknesses of this evidence, and hence the ability to synthesize it, has been limited by inadequate reporting of results. ⋯ The additions concern population stratification, genotyping errors, modelling haplotype variation, Hardy-Weinberg equilibrium, replication, selection of participants, rationale for choice of genes and variants, treatment effects in studying quantitative traits, statistical methods, relatedness, reporting of descriptive and outcome data, and the volume of data issues that are important to consider in genetic association studies. The STREGA recommendations do not prescribe or dictate how a genetic association study should be designed but seek to enhance the transparency of its reporting, regardless of choices made during design, conduct, or analysis.