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    <title>topic Re: what analysis to use? in Statistical Procedures</title>
    <link>https://communities.sas.com/t5/Statistical-Procedures/what-analysis-to-use/m-p/545528#M27285</link>
    <description>&lt;P&gt;From your description, Cochran's Q test might be appropriate:&lt;/P&gt;
&lt;P&gt;&amp;nbsp;&lt;/P&gt;
&lt;P&gt;&lt;A href="http://support.sas.com/documentation/cdl/en/statug/66859/HTML/default/viewer.htm#statug_freq_examples10.htm" target="_blank"&gt;http://support.sas.com/documentation/cdl/en/statug/66859/HTML/default/viewer.htm#statug_freq_examples10.htm&lt;/A&gt;&lt;/P&gt;</description>
    <pubDate>Sat, 23 Mar 2019 21:25:24 GMT</pubDate>
    <dc:creator>Norman21</dc:creator>
    <dc:date>2019-03-23T21:25:24Z</dc:date>
    <item>
      <title>what analysis to use?</title>
      <link>https://communities.sas.com/t5/Statistical-Procedures/what-analysis-to-use/m-p/542309#M27179</link>
      <description>&lt;P&gt;I have a data set with patients who have all had four samples taken during surgery (sample1-sample4). I want to find out if there is an increased risk of sample2-sample4 being positive if the sample1 is positive. Also if there is an increased risk of sample3 and sample4 sample being positive if&amp;nbsp; sample2 is positive and so on.&lt;/P&gt;&lt;P&gt;I suppose I have to use proc mixed by I don't know how to do it. I hope someone can point me in the right direction.&lt;/P&gt;&lt;P&gt;Thank you so much&lt;/P&gt;&lt;P&gt;&amp;nbsp;&lt;/P&gt;&lt;P&gt;ansepans&lt;/P&gt;</description>
      <pubDate>Tue, 12 Mar 2019 09:54:34 GMT</pubDate>
      <guid>https://communities.sas.com/t5/Statistical-Procedures/what-analysis-to-use/m-p/542309#M27179</guid>
      <dc:creator>ansepans</dc:creator>
      <dc:date>2019-03-12T09:54:34Z</dc:date>
    </item>
    <item>
      <title>Re: what analysis to use?</title>
      <link>https://communities.sas.com/t5/Statistical-Procedures/what-analysis-to-use/m-p/545528#M27285</link>
      <description>&lt;P&gt;From your description, Cochran's Q test might be appropriate:&lt;/P&gt;
&lt;P&gt;&amp;nbsp;&lt;/P&gt;
&lt;P&gt;&lt;A href="http://support.sas.com/documentation/cdl/en/statug/66859/HTML/default/viewer.htm#statug_freq_examples10.htm" target="_blank"&gt;http://support.sas.com/documentation/cdl/en/statug/66859/HTML/default/viewer.htm#statug_freq_examples10.htm&lt;/A&gt;&lt;/P&gt;</description>
      <pubDate>Sat, 23 Mar 2019 21:25:24 GMT</pubDate>
      <guid>https://communities.sas.com/t5/Statistical-Procedures/what-analysis-to-use/m-p/545528#M27285</guid>
      <dc:creator>Norman21</dc:creator>
      <dc:date>2019-03-23T21:25:24Z</dc:date>
    </item>
    <item>
      <title>Re: what analysis to use?</title>
      <link>https://communities.sas.com/t5/Statistical-Procedures/what-analysis-to-use/m-p/546250#M27313</link>
      <description>&lt;P&gt;Since your response is binary (positive vs negative), PROC MIXED is definitely not what you want to use since it expects a normal response. Cochran's Q tests that the marginal probabilities (probability of positive in each sample) are equal, but since you seem to really be after assessing conditional probabilities (such as probability of positive in sample 4 given result in sample3), I don't think it is what you want either. One thing you could consider are transition models as described in &lt;A href="http://support.sas.com/kb/24494" target="_self"&gt;this note&lt;/A&gt;.&amp;nbsp;&lt;/P&gt;</description>
      <pubDate>Tue, 26 Mar 2019 17:53:36 GMT</pubDate>
      <guid>https://communities.sas.com/t5/Statistical-Procedures/what-analysis-to-use/m-p/546250#M27313</guid>
      <dc:creator>StatDave</dc:creator>
      <dc:date>2019-03-26T17:53:36Z</dc:date>
    </item>
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