<html><head><meta http-equiv="content-type" content="text/html; charset=utf-8"></head><body dir="auto"><div>Nossos estatísticos está fazendo coisas importantes!!!!</div><div>Basílio<br><br>Enviado do meu iPhone</div><div><br>Início da mensagem encaminhada<br><br></div><blockquote type="cite"><div><b>De:</b> Giulio Flore <<a href="mailto:giulio.flore@googlemail.com">giulio.flore@googlemail.com</a>><br><b>Data:</b> 2 de junho de 2015 12:09:44 GMT+2<br><b>Para:</b> <a href="mailto:medstats@googlegroups.com">medstats@googlegroups.com</a><br><b>Assunto:</b> <b>Re: {MEDSTATS} Zero Inflated Cox Regression?</b><br><b>Responder A:</b> <a href="mailto:medstats@googlegroups.com">medstats@googlegroups.com</a><br><br></div></blockquote><blockquote type="cite"><div><div dir="ltr">No worries,<div><br></div><div>Good luck with the task</div></div><div class="gmail_extra"><br><div class="gmail_quote">On 2 June 2015 at 12:09, Tzippy Shochat <span dir="ltr"><<a href="mailto:tz.shochat@gmail.com" target="_blank">tz.shochat@gmail.com</a>></span> wrote:<br><blockquote class="gmail_quote" style="margin:0 0 0 .8ex;border-left:1px #ccc solid;padding-left:1ex"><div dir="rtl"><div dir="ltr">Thanks Giulio.<br></div><div dir="ltr"><br></div></div><div class="HOEnZb"><div class="h5"><div class="gmail_extra"><div dir="ltr"><br><div class="gmail_quote">2015-06-02 11:33 GMT+03:00 Giulio Flore <span dir="ltr"><<a href="mailto:giulio.flore@googlemail.com" target="_blank">giulio.flore@googlemail.com</a>></span>:<br><blockquote class="gmail_quote" style="margin:0 .8ex;border-left:1px #ccc solid;border-right:1px #ccc solid;padding-left:1ex;padding-right:1ex">For what is worth, you might want to have a look at<div><br></div><div><span style="font-family:Arial,sans-serif;font-size:13px;line-height:16.1200008392334px">Rodrigues, J., de Castro, M., Cancho, V. G., & Balakrishnan, N. (2009). COM–Poisson cure rate survival models and an application to a cutaneous melanoma data. </span><i style="font-family:Arial,sans-serif;font-size:13px;line-height:16.1200008392334px">Journal of Statistical Planning and Inference</i><span style="font-family:Arial,sans-serif;font-size:13px;line-height:16.1200008392334px">, </span><i style="font-family:Arial,sans-serif;font-size:13px;line-height:16.1200008392334px">139</i><span style="font-family:Arial,sans-serif;font-size:13px;line-height:16.1200008392334px">(10), 3605-3611. </span><span style="font-family:Arial,sans-serif;font-size:13px;line-height:16.1200008392334px">Free download is available on Scholar </span></div><div><span style="font-family:Arial,sans-serif;font-size:13px;line-height:16.1200008392334px"><br></span></div><div><font face="Arial, sans-serif"><span style="line-height:16.1200008392334px">The best bit about CMP is that it deals with zero inflated distribution as well as not over-dispered distributions like Poisson. A seminal article on the subject is </span></font></div><div><font face="Arial, sans-serif"><span style="line-height:16.1200008392334px"><br></span></font></div><div><span style="font-family:Arial,sans-serif;font-size:13px;line-height:16.1200008392334px">Cordeiro, G. M., Rodrigues, J., & de Castro, M. (2012). The exponential COM-Poisson distribution. </span><i style="font-family:Arial,sans-serif;font-size:13px;line-height:16.1200008392334px">Statistical papers</i><span style="font-family:Arial,sans-serif;font-size:13px;line-height:16.1200008392334px">, </span><i style="font-family:Arial,sans-serif;font-size:13px;line-height:16.1200008392334px">53</i><span style="font-family:Arial,sans-serif;font-size:13px;line-height:16.1200008392334px">(3), 653-664.</span><font face="Arial, sans-serif"><span style="line-height:16.1200008392334px"><br></span></font></div><div><span style="font-family:Arial,sans-serif;font-size:13px;line-height:16.1200008392334px"><br></span></div><div><span style="font-family:Arial,sans-serif;font-size:13px;line-height:16.1200008392334px">It is available from Springer. There was a paper on CMP also on one of the RSS journals. </span></div><div><span style="font-family:Arial,sans-serif;font-size:13px;line-height:16.1200008392334px"><br></span></div><div><span style="font-family:Arial,sans-serif;font-size:13px;line-height:16.1200008392334px">The added bonus is that CMP regression is available in STATA and in R.</span></div><div><span style="font-family:Arial,sans-serif;font-size:13px;line-height:16.1200008392334px"><br></span></div><div><span style="font-family:Arial,sans-serif;font-size:13px;line-height:16.1200008392334px">Best of luck,</span></div><div><span style="font-family:Arial,sans-serif;font-size:13px;line-height:16.1200008392334px"><br></span></div><div><span style="font-family:Arial,sans-serif;font-size:13px;line-height:16.1200008392334px">Giulio</span></div><div><span style="font-family:Arial,sans-serif;font-size:13px;line-height:16.1200008392334px"><br></span></div><div><div><br></div><div style="font-family:Arial,sans-serif;font-size:13px;line-height:16.1200008392334px"><br></div></div><div><span style="font-family:Arial,sans-serif;font-size:13px;line-height:16.1200008392334px"> </span></div></blockquote></div><div><div><div class="gmail_extra"><br><div class="gmail_quote">On 2 June 2015 at 09:55, Tzippy Shochat <span dir="ltr"><<a href="mailto:tz.shochat@gmail.com" target="_blank">tz.shochat@gmail.com</a>></span> wrote:<br><blockquote class="gmail_quote" style="margin:0 0 0 .8ex;border-left:1px #ccc solid;padding-left:1ex"><div dir="rtl"><div dir="ltr">Dear All.<br><br></div><div dir="ltr">A common situation in Cancer research is measuring the Duration of Response, which is measured as the time from clinical response to treatment till the time of disease progression or death.<br><br></div><div dir="ltr">The problem is that not all patients have a clinical response and there is no clear consensus how to treat these subjects.<br><br></div><div dir="ltr">One option I've seen used is to do a two part analysis: Logistic Regression for probability of reponse, followed by Cox regression, where non-responders are deleted from the model.<br><br></div><div dir="ltr">Another option is to assing a zero duration of response time to non-responders and treat these patients as censored at time "0".<br><br></div><div dir="ltr">I found one refference to a Zero Inflated Cox Regression (see below) but I'm not sure if this approach has gained acceptence in medical research.<br></div><div dir="ltr"><br><p class="MsoNormal" dir="RTL"><span dir="LTR"><a href="http://sites.uclouvain.be/IAP-Stat-Phase-V-VI/PhaseVI/publications_2007/TR/TR07034.pdf" target="_blank">http://sites.uclouvain.be/IAP-Stat-Phase-V-VI/PhaseVI/publications_2007/TR/TR07034.pdf</a></span></p>
<span dir="LTR"></span><br></div><div dir="ltr">Any thoughts, examples would be greatly appreciated.<br><br></div><div dir="ltr">Tzippy Shochat<span><font color="#888888"><br></font></span></div></div><span><font color="#888888">
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