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(This article was first published on <strong><a href="http://statistica.it/post/2018-09-10-survhe-update/"> R on Gianluca Baio</a></strong>, and kindly contributed to <a rel="nofollow" href="https://www.r-bloggers.com/">R-bloggers)</a> </div>
<p>Because I have been preparing an extended presentation on (Bayesian) survival analysis in health economic evaluation, I took the opportunity to make some tweaks to <a rel="nofollow" href="http://www.statistica.it/gianluca/software/survhe/" target="_blank">survHE</a> — nothing major, but I was aware of couple of imprecisions in the code or things I wanted to make a bit better, so while I was <a rel="nofollow" href="https://yihui.name/knitr/" target="_blank">knitr</a>ing my slides, I made the changes.</p>
<p>As soon as I get a moment, I’ll also update my repository (which can be used to update the package), but for now, I’ve uploaded the changes on <a rel="nofollow" href="https://github.com/giabaio/survHE" target="_blank">GitHub</a>, so the development version (1.0.63) is now available by simply typing</p>
<pre><code>devtools::update_packages(&quot;survHE&quot;)
</code></pre>
<p>on the <tt>R</tt> terminal (assuming you have installed <tt>survHE</tt>).</p>
<p>The changes are the following:</p>
<ol>
<li>
<p>I’ve fixed a minor problem in the <tt>print</tt> method; up until now, if you were fitting an Exponential model with no covariates using MLE (ie via <a rel="nofollow" href="https://cran.r-project.org/web/packages/flexsurv/flexsurv.pdf" target="_blank"><tt>flexsurv</tt></a>), then the resulting output from the call to <tt>print</tt> was simply showing the values (mean, 95% confidence interval and SE) but no label for the relevant parameter (it should have read <tt>rate</tt>). The <tt>print</tt> method was doing OK when the underlying model was run using HMC (via <a rel="nofollow" href="http://mc-stan.org/users/interfaces/rstan" target="_blank"><tt>rstan</tt></a>) or INLA. Now it consistently report the name of the parameters.</p>
</li>
<li>
<p>I’ve made some addition to the <tt>model.fit.plot</tt>, which can be used to visualise the model fitting using Information Criteria, as recommended by the <a rel="nofollow" href="http://nicedsu.org.uk/wp-content/uploads/2016/03/NICE-DSU-TSD-Survival-analysis.updated-March-2013.v2.pdf" target="_blank">NICE DSU</a>. Now, there’s an additional option <tt>scale</tt>, which by default is set to <tt>“absolute”</tt> (and nothing changes in comparison to the previous version) but can now also take on the value <tt>“relative”</tt> (or <tt>“rel”</tt>). This in turn creates barplots with the percentage increase in the *IC selected with comparison to the best fitting model (among the lot selected). This makes it easier to check how much better the “better” model is.</p>
</li>
</ol>
<p>I may make a few more changes in the next few days, as I keep working on this — and if so, I’ll update the GitHub repository.</p>
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