Bayesian Biostatistics

Part 1 General overview: Bayesian methods in health-related research; Bayesian approaches to randomized trials; Bayesian epidemiology. Part 2 Assessing probabilities: elicitation of prior distributions; priors for the design and analysis of clinical trials. Part 3 Decision problems: a Weibull model for survival data - using prediction to decide when to stop a clinical trial; decision models in clinical recommendations development - the stroke prevention policy model; dose-response analysis of toxic chemicals; expected utility as a policy making tool - an environmental health example. Part 4 Design: Bayesian hypothesis testing - interim analysis of a clinical trial evaluating phenytoin for the prophylaxis of early post-traumatic seizures in children; inference and design strategies for a hierarchical logistic regression model. Part 5 Model selection: model selection for generalized linear models via GLIB - application to nutrition and breast cancer. Part 6 Hierarchical models: Bayesian analysis of population pharmacokinetic and instantaneous pharmacodynamic relationships; Bayesian and frequentist analysis of an in vivo experiment in tumor hemodynamics; Bayesian meta-analysis of randomized trials using graphical models for assessing the effect of extreme cold weather on schizophrenic births; fitting and checking a two-level Poisson model - modelling patient mortality rates in heart transplant patients. Part 7 Other topics: analyzing rodent tumorigencitiy experiments using expert knowledge; assessing drug interactions - tamoxifen and cyclophosphamide; Bayesian subset analysis of a clinical trial for the treatment of HIV infections; Bayesian modelling of binary repeated measures data with application to crossover trials; a comparative study of perinatal mortality using a two-component mixture model; change-point analysis of a randomized trial on the effects of calcium supplementation on blood pressure; Bayesian predictive inference for a binary random variable - survey