<p><div><div><div><div data-width="100" data-show-faces="false" data-send="true" data-layout="button_count" data-height="21" data-href="http://www.r-bloggers.com/bcea-2-2-3-is-out/"></div></div><div><a data-url="http://www.r-bloggers.com/bcea-2-2-3-is-out/" data-counturl="http://www.r-bloggers.com/bcea-2-2-3-is-out/" data-count="horizontal" data-text="BCEA 2.2-3 is out" class="twitter-share-button" data-via="rbloggers" href="https://twitter.com/share"></a></div><div></div></div><div></div></div>
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(This article was first published on <strong><a href="http://gianlubaio.blogspot.com/2016/05/bcea-22-3-is-out.html"> Gianluca Baio's blog</a></strong>, and kindly contributed to <a rel="nofollow" href="http://www.r-bloggers.com/">R-bloggers)</a> </div>
<p><span>I think the newest release of <a target="_blank" href="http://www.statistica.it/gianluca/BCEA">BCEA</a>, our R package to standardise and post-process the output of a health economic model, is now available from <a target="_blank" href="http://cran.r-project.org/">CRAN</a> $-$ in fact, the source code is also available <a target="_blank" href="http://www.statistica.it/gianluca/BCEA/BCEA_2.2-3.tar.gz">here</a>. </span><span>The package is rather stable, so the changes aren’t many, but the few ones are quite substantial, I think. In particular, we’ve now modified the function </span><span>evppi</span><span>, which is used to perform the analysis of the expected value of partial information (incidentally, that’s also related to our upcoming <a target="_blank" href="https://www.ucl.ac.uk/statistics/research/statistics-health-economics/Short_course_VOI">short course</a>). </span><br><span><br></span><span>In the last few years this has been a very interesting and fertile area of research within the health economics community, with interesting methods being proposed $-$ <a target="_blank" href="http://mdm.sagepub.com/content/35/5/564.long">this</a> is a nice editorial by <a target="_blank" href="http://www.bristol.ac.uk/social-community-medicine/people/nicky-j-welton/index.html">Nicky Welton</a> and <a target="_blank" href="http://www.bristol.ac.uk/social-community-medicine/people/howard-h-thom/index.html">Howard Thom</a>, while <a target="_blank" href="http://arxiv.org/pdf/1507.02513v1.pdf">this</a> is (an arxived version of) our own technical review.</span><br><span><br></span><span>BCEA implements all the most recent methods, with particular focus on <a target="_blank" href="http://mdm.sagepub.com/content/34/3/311.full.pdf+html">Strong et al</a>‘s based on Gaussian Process regression and our <a target="_blank" href="http://onlinelibrary.wiley.com/doi/10.1002/sim.6983/full">own work</a> (just published in <i><a target="_blank" href="http://onlinelibrary.wiley.com/journal/10.1002/(ISSN)1097-0258">Statistics in Medicine</a></i>), which, building on their work, uses <a target="_blank" href="http://r-inla.org/">INLA</a> to speed up the computation even further. In addition, we have also included a graphical tool that can be used to describe, at least as a first order approximation, the individual impact of each parameter on the overall uncertainty in the decision-making process. We have called this the <i>info-rank</i> plot, which is basically a generalisation of commonly used (especially when economic evaluations are performed under a frequentist approach) <a target="_blank" href="https://en.wikipedia.org/wiki/Tornado_diagram">Tornado plots</a>. The info-rank is based on the single-parameter EVPPI and can be used to roughly determine the contribution of each single parameters to the overall value of partial information (of course, because the EVPPI is a highly non-linear function, combinations of parameters are not additive, so some caution is needed here).</span>
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<div>To <strong>leave a comment</strong> for the author, please follow the link and comment on their blog: <strong><a href="http://gianlubaio.blogspot.com/2016/05/bcea-22-3-is-out.html"> Gianluca Baio's blog</a></strong>.</div>
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