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    <title>topic Re: multiple outcome comparison with proc mixed in Statistical Procedures</title>
    <link>https://communities.sas.com/t5/Statistical-Procedures/multiple-outcome-comparison-with-proc-mixed/m-p/533607#M26869</link>
    <description>&lt;P&gt;Are the observations correlated across the responses?&amp;nbsp; ie, did you measure the 5 outcomes on the same experimental unit, with units divided into two group?&amp;nbsp; A repeated measures model might work then.&amp;nbsp; You can use PROC GLIMMIX, fitting a separate mean to each level of response and you could compare the means using an LSMEANS statement with SLICEDIFF= and the appropriate ADJUST=.&amp;nbsp; &amp;nbsp;&amp;nbsp;&lt;/P&gt;
&lt;P&gt;&amp;nbsp;&lt;/P&gt;
&lt;P&gt;Something like&lt;/P&gt;
&lt;P&gt;&lt;BR /&gt;proc glimmix data=test;&lt;BR /&gt;&amp;nbsp; &amp;nbsp;class group resp;&amp;nbsp;&lt;BR /&gt;&amp;nbsp; &amp;nbsp;model y=group*resp;&lt;/P&gt;
&lt;P&gt;&amp;nbsp; &amp;nbsp;random int / subject=id;&lt;BR /&gt;&amp;nbsp; &amp;nbsp;lsmeans group*resp / slicediff=resp adjust=tukey;&lt;BR /&gt;run;&lt;/P&gt;
&lt;P&gt;&amp;nbsp;&lt;/P&gt;
&lt;P&gt;might work.&amp;nbsp; MIXED does not have the ability to adjust across only the sliced tests.&lt;/P&gt;</description>
    <pubDate>Thu, 07 Feb 2019 15:09:45 GMT</pubDate>
    <dc:creator>StatsMan</dc:creator>
    <dc:date>2019-02-07T15:09:45Z</dc:date>
    <item>
      <title>multiple outcome comparison with proc mixed</title>
      <link>https://communities.sas.com/t5/Statistical-Procedures/multiple-outcome-comparison-with-proc-mixed/m-p/532167#M26832</link>
      <description>&lt;P&gt;Hi SAS users,&lt;/P&gt;&lt;P&gt;&amp;nbsp;&lt;/P&gt;&lt;P&gt;I am trying to use proc mixed to run repeated measurement analyses for 5 continuous&amp;nbsp;outcomes comparing two independent groups.&amp;nbsp;&lt;/P&gt;&lt;P&gt;&amp;nbsp;&lt;/P&gt;&lt;P&gt;I am also want to do adjustment (such as Tukey) because I am testing several outcomes.&amp;nbsp;&amp;nbsp;&lt;/P&gt;&lt;P&gt;&amp;nbsp;&lt;/P&gt;&lt;P&gt;How can I write the code using proc mixed procedure to achieve those objectives?&lt;/P&gt;&lt;P&gt;&amp;nbsp;&lt;/P&gt;&lt;P&gt;Thank you.&lt;/P&gt;&lt;P&gt;&amp;nbsp;&lt;/P&gt;&lt;P&gt;&amp;nbsp;&lt;/P&gt;&lt;P&gt;&amp;nbsp;&lt;/P&gt;</description>
      <pubDate>Fri, 01 Feb 2019 19:22:39 GMT</pubDate>
      <guid>https://communities.sas.com/t5/Statistical-Procedures/multiple-outcome-comparison-with-proc-mixed/m-p/532167#M26832</guid>
      <dc:creator>superbibi</dc:creator>
      <dc:date>2019-02-01T19:22:39Z</dc:date>
    </item>
    <item>
      <title>Re: multiple outcome comparison with proc mixed</title>
      <link>https://communities.sas.com/t5/Statistical-Procedures/multiple-outcome-comparison-with-proc-mixed/m-p/533607#M26869</link>
      <description>&lt;P&gt;Are the observations correlated across the responses?&amp;nbsp; ie, did you measure the 5 outcomes on the same experimental unit, with units divided into two group?&amp;nbsp; A repeated measures model might work then.&amp;nbsp; You can use PROC GLIMMIX, fitting a separate mean to each level of response and you could compare the means using an LSMEANS statement with SLICEDIFF= and the appropriate ADJUST=.&amp;nbsp; &amp;nbsp;&amp;nbsp;&lt;/P&gt;
&lt;P&gt;&amp;nbsp;&lt;/P&gt;
&lt;P&gt;Something like&lt;/P&gt;
&lt;P&gt;&lt;BR /&gt;proc glimmix data=test;&lt;BR /&gt;&amp;nbsp; &amp;nbsp;class group resp;&amp;nbsp;&lt;BR /&gt;&amp;nbsp; &amp;nbsp;model y=group*resp;&lt;/P&gt;
&lt;P&gt;&amp;nbsp; &amp;nbsp;random int / subject=id;&lt;BR /&gt;&amp;nbsp; &amp;nbsp;lsmeans group*resp / slicediff=resp adjust=tukey;&lt;BR /&gt;run;&lt;/P&gt;
&lt;P&gt;&amp;nbsp;&lt;/P&gt;
&lt;P&gt;might work.&amp;nbsp; MIXED does not have the ability to adjust across only the sliced tests.&lt;/P&gt;</description>
      <pubDate>Thu, 07 Feb 2019 15:09:45 GMT</pubDate>
      <guid>https://communities.sas.com/t5/Statistical-Procedures/multiple-outcome-comparison-with-proc-mixed/m-p/533607#M26869</guid>
      <dc:creator>StatsMan</dc:creator>
      <dc:date>2019-02-07T15:09:45Z</dc:date>
    </item>
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