Bayesian BiostatisticsThe growth of biostatistics has been phenomenal in recent years and has been marked by considerable technical innovation in both methodology and computational practicality. One area that has experienced significant growth is Bayesian methods. The growing use of Bayesian methodology has taken place partly due to an increasing number of practitioners valuing the Bayesian paradigm as matching that of scientific discovery. In addition, computational advances have allowed for more complex models to be fitted routinely to realistic data sets. Through examples, exercises and a combination of introductory and more advanced chapters, this book provides an invaluable understanding of the complex world of biomedical statistics illustrated via a diverse range of applications taken from epidemiology, exploratory clinical studies, health promotion studies, image analysis and clinical trials. Key Features:
Bayesian Biostatistics introduces the reader smoothly into the Bayesian statistical methods with chapters that gradually increase in level of complexity. Master students in biostatistics, applied statisticians and all researchers with a good background in classical statistics who have interest in Bayesian methods will find this book useful. |
Contents
Bayes theorem Computing the posterior | |
Introduction to Bayesian inference | |
More than one parameter | |
Choosing the prior distribution | |
Markov chain Monte Carlo sampling | |
Assessing and improving convergence of | |
Model building and assessment | |
Variable selection | |
Bioassay | |
Measurement error | |
Survival analysis | |
Longitudinal analysis | |
Spatial applications Disease mapping | |
Final chapter | |
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Common terms and phrases
assumed Bayes factor Bayes theorem Bayesian analysis Bayesian approach Bayesian LASSO Bayesian model chapter classical computed conjugate prior convergence correlation covariates credible interval diagnostic disease ECASS estimates evaluated Example Exercise expression Figure flat prior frequentist full conditionals function gamma Gaussian Gelman Gibbs sampler given hierarchical model improper priors independent inference Jeffreys prior Journal likelihood linear regression lip cancer marginal likelihood marginal posterior Markov chain matrix MCMC measurement error methods MH algorithm model selection Monte Carlo multivariate noninformative prior normal distribution normal prior observed obtained Osteoporosis Osteoporosis study P-value patients Poisson posterior distribution posterior mean posterior probability posterior summary measures prior distribution prior information proposal density random effects random intercept random variables regression coefficients regression model regression parameters regressors RJMCMC sampling SAS procedure Section spatial specific standard techniques trace plot values variable selection vector WinBUGS WinBUGS program zero