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    <title>topic Re: Incidence Risk Ratio (IRR) in Statistical Procedures</title>
    <link>https://communities.sas.com/t5/Statistical-Procedures/Incidence-Risk-Ratio-IRR/m-p/372269#M19493</link>
    <description>&lt;P&gt;A negative binomial or ZINB model is for a count response. &amp;nbsp;A risk ratio is a ratio of event probabilities, so that assumes that the response is binary, not a count. &amp;nbsp;Assuming your response is really a count, then you might want to estimate a RATE ratio. &lt;A href="http://support.sas.com/kb/24188" target="_self"&gt;This note&lt;/A&gt; discusses estimating rates and rate ratios with a count model. &lt;A href="http://support.sas.com/kb/44354" target="_self"&gt;This note&lt;/A&gt; talks about estimating rates using zero-inflated count models.&amp;nbsp;&lt;/P&gt;</description>
    <pubDate>Fri, 30 Jun 2017 18:03:40 GMT</pubDate>
    <dc:creator>StatDave</dc:creator>
    <dc:date>2017-06-30T18:03:40Z</dc:date>
    <item>
      <title>Incidence Risk Ratio (IRR)</title>
      <link>https://communities.sas.com/t5/Statistical-Procedures/Incidence-Risk-Ratio-IRR/m-p/370995#M19449</link>
      <description>&lt;P&gt;Hi everyone&lt;/P&gt;&lt;P&gt;I am trying to get the incience risk ratio (irr) for each of my independent variables while also using the zero inflated negative binomial regression. How can i do this as the output for ZINB does not give the irr.&lt;/P&gt;&lt;P&gt;Thanks&lt;/P&gt;</description>
      <pubDate>Tue, 27 Jun 2017 16:33:26 GMT</pubDate>
      <guid>https://communities.sas.com/t5/Statistical-Procedures/Incidence-Risk-Ratio-IRR/m-p/370995#M19449</guid>
      <dc:creator>AY_MANSA</dc:creator>
      <dc:date>2017-06-27T16:33:26Z</dc:date>
    </item>
    <item>
      <title>Re: Incidence Risk Ratio (IRR)</title>
      <link>https://communities.sas.com/t5/Statistical-Procedures/Incidence-Risk-Ratio-IRR/m-p/371578#M19468</link>
      <description>&lt;P&gt;In my opinion, you should forget this idea and fit data with Poisson regression.&lt;/P&gt;
&lt;P&gt;This is because the likelihood obtained from observing person time and events is&amp;nbsp;the same function of the parameters as when you have poisson distributed counts. Therefore, Poisson regression is just used as a trick to maximize the likehood function and thereby find the right estimates of the IRR. It doesnt matter that the fitstatistics shows a misfit, as the data was not Poisson distributed anyway.&lt;/P&gt;
&lt;P&gt;Negative binomial regression will only result in biased standard errors.&lt;/P&gt;</description>
      <pubDate>Thu, 29 Jun 2017 09:00:18 GMT</pubDate>
      <guid>https://communities.sas.com/t5/Statistical-Procedures/Incidence-Risk-Ratio-IRR/m-p/371578#M19468</guid>
      <dc:creator>JacobSimonsen</dc:creator>
      <dc:date>2017-06-29T09:00:18Z</dc:date>
    </item>
    <item>
      <title>Re: Incidence Risk Ratio (IRR)</title>
      <link>https://communities.sas.com/t5/Statistical-Procedures/Incidence-Risk-Ratio-IRR/m-p/372269#M19493</link>
      <description>&lt;P&gt;A negative binomial or ZINB model is for a count response. &amp;nbsp;A risk ratio is a ratio of event probabilities, so that assumes that the response is binary, not a count. &amp;nbsp;Assuming your response is really a count, then you might want to estimate a RATE ratio. &lt;A href="http://support.sas.com/kb/24188" target="_self"&gt;This note&lt;/A&gt; discusses estimating rates and rate ratios with a count model. &lt;A href="http://support.sas.com/kb/44354" target="_self"&gt;This note&lt;/A&gt; talks about estimating rates using zero-inflated count models.&amp;nbsp;&lt;/P&gt;</description>
      <pubDate>Fri, 30 Jun 2017 18:03:40 GMT</pubDate>
      <guid>https://communities.sas.com/t5/Statistical-Procedures/Incidence-Risk-Ratio-IRR/m-p/372269#M19493</guid>
      <dc:creator>StatDave</dc:creator>
      <dc:date>2017-06-30T18:03:40Z</dc:date>
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