Bayesian Analysis with Stata

Bayesian Analysis with Stata is written for anyone interested in applying Bayesian methods to real data easily. The book shows how modern analyses based on Markov chain Monte Carlo (MCMC) methods are implemented in Stata both directly and by passing Stata datasets to OpenBUGS or WinBUGS for computation, allowing Stata’s data management and graphing capability to be used with OpenBUGS/WinBUGS speed and reliability. The book emphasizes practical data analysis from the Bayesian perspective, and hence covers the selection of realistic priors, computational efficiency and speed, the assessment of convergence, the evaluation of models, and the presentation of the results. Every topic is illustrated in detail using real-life examples, mostly drawn from medical research. The book takes great care in introducing concepts and coding tools incrementally so that there are no steep patches or discontinuities in the learning curve. The book's content helps the user see exactly what computations are done for simple standard models and shows the user how those computations are implemented. Understanding these concepts is important for users because Bayesian analysis lends itself to custom or very complex models, and users must be able to code these themselves.

List of figures List of tables Preface Acknowledgments The problem of priors Case study 1: An early phase vaccine trial Bayesian calculations Benefits of a Bayesian analysis Selecting a good prior Starting points Exercises Evaluating the posterior Introduction Case study 1: The vaccine trial revisited Marginal and conditional distributions Case study 2: Blood pressure and age Case study 2: BP and age continued General log posteriors Adding distributions to logdensity Changing parameterization Starting points Exercises Metropolis–Hastings Introduction The MH algorithm in Stata The mhs commands Case study 3: Polyp counts Scaling the proposal distribution The mcmcrun command Multiparameter models Case study 3: Polyp counts continued Highly correlated parameters Case study 3: Polyp counts yet again Starting points Exercises Gibbs sampling Introduction Case study 4: A regression model for pain scores Conjugate priors Gibbs sampling with nonstandard distributions The gbs commands Case study 4 continued: Laplace regression Starting points Exercises Assessing convergence Introduction Detecting early drift Detecting too short a run Running multiple chains Convergence of functions of the parameters Case study 5: Beta-blocker trials Further reading Exercises Validating the Stata code and summarizing the results Introduction Case study 6: Ordinal regression Validating the software Numerical summaries Graphical summaries Further reading Exercises Bayesian analysis with Mata Introduction The basics of Mata Case study 6: Revisited Case study 7: Germination of broomrape Further reading Exercises Using WinBUGS for model fitting Introduction Installing the software Preparing a WinBUGS analysis Case study 8: Growth of sea cows Case study 9: Jawbone size Advanced features of WinBUGS GeoBUGS Programming a series of Bayesian analyses OpenBUGS under Linux Debugging WinBUGS Starting points Exercises Model checking Introduction Bayesian residual analysis The mcmccheck command Case study 10: Models for Salmonella assays Residual checking with Stata Residual checking with Mata Further reading Exercises Model selection Introduction Case study 11: Choosing a genetic model Calculating a BF Calculating the BFs for the NTD case study Robustness of the BF Model averaging Information criteria DIC for the genetic models Starting points Exercises Further case studies Introduction Case study 12: Modeling cancer incidence Case study 13: Creatinine clearance Case study 14: Microarray experiment Case study 15: Recurrent asthma attacks Exercises Writing Stata programs for specific Bayesian analysis Introduction The Bayesian lasso The Gibbs sampler The Mata code A Stata ado-file Testing the code Case study 16: Diabetes data Extensions to the Bayesian lasso program Exercises A Standard distributions References Author index Subject index

John Thompson is professor of genetic epidemiology at the University of Leicester and has many years experience working as a biostatistician on epidemiological projects.