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Table of contents
Preface
Get a promotional code - save 20% when ordering online from CRC website Read a sample for free BCEA - an R package to run Bayesian health economic evaluations (used throughout the book and specifically in the examples) Some discussion of the book in the blog can be found here, here, here and here The book has received excellent reviews, for example by Patrick Graham

BCEA is a R library specifically designed to post-process the result of a Bayesian health economic evaluation. Typically, this consists in the estimation of a set of relevant parameters that can be combined to produce an estimation of suitable measures of cost (\(c\)) and clinical benefits (\(e\)) associated with an intervention. Within the Bayesian framework, this amounts to estimating a posterior distribution for the pair \((e,c)\).
Health economic evaluations then proceed by computing some relevant summaries of the resulting decision process: is the innovative intervention \(t=1\) more “cost-effective” than the standard intervention \(t=0\)?

Introduction The intention of this vignette is to show how to plot different styles of cost-effectiveness acceptability curves using the BCEA package.
Two interventions only This is the simplest case, usually an alternative intervention (\(i=1\)) versus status-quo (\(i=0\)).
The plot show the probability that the alternative intervention is cost-effective for each willingness to pay, \(k\),
\[ p(NB_1 \geq NB_0 | k) \mbox{ where } NB_i = ke - c \]

There are several arguments passed to bcea() to specify the form of the analysis. These are
bcea(e, c, ref = 1, interventions = NULL, .comparison = NULL, Kmax = 50000, wtp = NULL, plot = FALSE) Those of interest here are:
ref is the reference intervention group to compare against the other groups. .comparisons are the groups to compare against ref. The default is all of the non-ref groups.

Check out our book on the BCEA R package published by Springer in the UseR! series. This book is co-authored by myself, Andrea Berardi and Anna Heath.
Table of contents Preface Get a promotional code - save 20% when ordering online from Springer website Read a sample for free BCEA - an R package to run Bayesian health economic evaluations (used throughout the book and specifically in the examples) Journal editors, journalists or bloggers can request a free Online Review Copy of the book.

Survival analysis in health economic evaluation Contains a suite of functions to streamline systematically the workflow involving survival analysis in health economic evaluation. survHE can fit a large range of survival models using both a frequentist approach (by calling the R package flexsurv) and a Bayesian perspective. For a selected range of models, both Integrated Nested Laplace Integration (via the R package INLA) and Hamiltonian Monte Carlo (HMC; via the R package rstan) are possible.

This tutorial is based on the Journal of Statistical Software paper describing the philosophy underlying survHE and its main functionality. All the technical details (including the statistical background) are described in the paper, while this tutorial is only meant to provide some annotated description of the main important functions provided in survHE.
The example uses a fictional dataset — in fact the actual data are created by “digitising” an existing Kaplan Meier curve.

Bayesian Methods in Pharmaceutical Research, edited by Emmanuel Lesaffre, Bruno Boulanger and Gianluca Baio and published by CRC, collates contributions by leading researchers in the various aspects of Bayesian modelling within the pharmaceutical industry.
The book covers all main areas of pharmaceutical development, from pre-clinical to post-marketing studies, highlighting the Bayesian contributions and advantages and will be out in early 2020. More to come in here too!

Chapter 23: Bayesian methods for in vitro dissolution drug testing and similarity comparisons

bmeta is a R package that provides a collection of functions for conducting meta-analyses and meta-regressions under a Bayesian context, using JAGS. The package includes functions for computing various effect size or outcome measures (e.g. odds ratios, mean difference and incidence rate ratio) for different types of data (e.g. binary, continuous and count, respectively), based on Markov Chain Monte Carlo (MCMC) simulations. Users are allowed to select fixed- and random-effects models with different prior distributions for the data and the relevant parameters.