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    <title>topic Help with longitudinal, overdispersed, count data via PROC GLIMMIX in SAS Programming</title>
    <link>https://communities.sas.com/t5/SAS-Programming/Help-with-longitudinal-overdispersed-count-data-via-PROC-GLIMMIX/m-p/794570#M254787</link>
    <description>&lt;P&gt;Hi all,&lt;/P&gt;&lt;P&gt;I'm relatively new to the world of SAS - I have some experience running PROC MIXED but have not needed to use SAS all too much throughout my graduate training. Here's a summary of my data that is being used for my dissertation:&lt;/P&gt;&lt;P&gt;- Approximately 50 youth, diagnosed with certain mental health disorders, who were a part of an intensive, 5-week treatment program&lt;/P&gt;&lt;P&gt;- The outcome of interest is aggression which is a continuous, non-negative, count variable and is&amp;nbsp;showing a negative binomial distribution (highly positively skewed with var &amp;gt; mean). This outcome was measured across the 5 weeks - thus, each child has a weekly aggression score&lt;/P&gt;&lt;P&gt;- This outcome is showing a highly skewed distribution across the 5 weeks&lt;/P&gt;&lt;P&gt;- I am interested in&amp;nbsp;whether certain pre-treatment affective states (e.g., irritability) predict changes in the intercept and slope/trajectory of aggression across the 5 treatment weeks&lt;/P&gt;&lt;P&gt;- Importantly, treatment did NOT differ across youth; they all received the same treatment&lt;/P&gt;&lt;P&gt;&amp;nbsp;&lt;/P&gt;&lt;P&gt;The various resources I have read online suggest PROC GLIMMIX to be the ideal approach but the issue I am running into is that every single resource/example implies that GLIMMIX is best suited for clustered data where participants are separated into various conditions. Again, that is not the case for my data. I have scores nested within youth but that is it.&amp;nbsp;&lt;/P&gt;&lt;P&gt;&amp;nbsp;&lt;/P&gt;&lt;P&gt;I've consulted various resources and have piecemealed syntax that runs a converging model without errors (all predictors have been centered hence the 'c'):&lt;/P&gt;&lt;P&gt;PROC GLIMMIX DATA = diss.stplong method=quad;&lt;BR /&gt;Class STPID;&lt;BR /&gt;Model CPsum = cirr clpe cweek / s link=log dist=negbin;&lt;BR /&gt;Random intercept / sub = STPID;&lt;BR /&gt;Run;&lt;/P&gt;&lt;P&gt;I removed the random effect of week from the RANDOM statement as that led to the estimated G matrix being not positive definite. I'd appreciate any/all feedback. Apologies for the long post and sorry for any elementary mistakes I've made in this.&lt;/P&gt;&lt;P&gt;Thanks so much.&lt;/P&gt;&lt;P&gt;-Pev.&lt;/P&gt;</description>
    <pubDate>Fri, 04 Feb 2022 17:34:57 GMT</pubDate>
    <dc:creator>PSB</dc:creator>
    <dc:date>2022-02-04T17:34:57Z</dc:date>
    <item>
      <title>Help with longitudinal, overdispersed, count data via PROC GLIMMIX</title>
      <link>https://communities.sas.com/t5/SAS-Programming/Help-with-longitudinal-overdispersed-count-data-via-PROC-GLIMMIX/m-p/794570#M254787</link>
      <description>&lt;P&gt;Hi all,&lt;/P&gt;&lt;P&gt;I'm relatively new to the world of SAS - I have some experience running PROC MIXED but have not needed to use SAS all too much throughout my graduate training. Here's a summary of my data that is being used for my dissertation:&lt;/P&gt;&lt;P&gt;- Approximately 50 youth, diagnosed with certain mental health disorders, who were a part of an intensive, 5-week treatment program&lt;/P&gt;&lt;P&gt;- The outcome of interest is aggression which is a continuous, non-negative, count variable and is&amp;nbsp;showing a negative binomial distribution (highly positively skewed with var &amp;gt; mean). This outcome was measured across the 5 weeks - thus, each child has a weekly aggression score&lt;/P&gt;&lt;P&gt;- This outcome is showing a highly skewed distribution across the 5 weeks&lt;/P&gt;&lt;P&gt;- I am interested in&amp;nbsp;whether certain pre-treatment affective states (e.g., irritability) predict changes in the intercept and slope/trajectory of aggression across the 5 treatment weeks&lt;/P&gt;&lt;P&gt;- Importantly, treatment did NOT differ across youth; they all received the same treatment&lt;/P&gt;&lt;P&gt;&amp;nbsp;&lt;/P&gt;&lt;P&gt;The various resources I have read online suggest PROC GLIMMIX to be the ideal approach but the issue I am running into is that every single resource/example implies that GLIMMIX is best suited for clustered data where participants are separated into various conditions. Again, that is not the case for my data. I have scores nested within youth but that is it.&amp;nbsp;&lt;/P&gt;&lt;P&gt;&amp;nbsp;&lt;/P&gt;&lt;P&gt;I've consulted various resources and have piecemealed syntax that runs a converging model without errors (all predictors have been centered hence the 'c'):&lt;/P&gt;&lt;P&gt;PROC GLIMMIX DATA = diss.stplong method=quad;&lt;BR /&gt;Class STPID;&lt;BR /&gt;Model CPsum = cirr clpe cweek / s link=log dist=negbin;&lt;BR /&gt;Random intercept / sub = STPID;&lt;BR /&gt;Run;&lt;/P&gt;&lt;P&gt;I removed the random effect of week from the RANDOM statement as that led to the estimated G matrix being not positive definite. I'd appreciate any/all feedback. Apologies for the long post and sorry for any elementary mistakes I've made in this.&lt;/P&gt;&lt;P&gt;Thanks so much.&lt;/P&gt;&lt;P&gt;-Pev.&lt;/P&gt;</description>
      <pubDate>Fri, 04 Feb 2022 17:34:57 GMT</pubDate>
      <guid>https://communities.sas.com/t5/SAS-Programming/Help-with-longitudinal-overdispersed-count-data-via-PROC-GLIMMIX/m-p/794570#M254787</guid>
      <dc:creator>PSB</dc:creator>
      <dc:date>2022-02-04T17:34:57Z</dc:date>
    </item>
    <item>
      <title>Re: Help with longitudinal, overdispersed, count data via PROC GLIMMIX</title>
      <link>https://communities.sas.com/t5/SAS-Programming/Help-with-longitudinal-overdispersed-count-data-via-PROC-GLIMMIX/m-p/794657#M254807</link>
      <description>&lt;P&gt;You are better post it at Stat Forum.&lt;/P&gt;
&lt;P&gt;&amp;nbsp;&lt;/P&gt;
&lt;P&gt;&lt;A href="https://communities.sas.com/t5/Statistical-Procedures/bd-p/statistical_procedures" target="_blank"&gt;https://communities.sas.com/t5/Statistical-Procedures/bd-p/statistical_procedures&lt;/A&gt;&lt;/P&gt;
&lt;P&gt;&amp;nbsp;&lt;/P&gt;
&lt;P&gt;and calling&amp;nbsp;&lt;a href="https://communities.sas.com/t5/user/viewprofilepage/user-id/13633"&gt;@StatDave&lt;/a&gt;&amp;nbsp;&amp;nbsp;&lt;a href="https://communities.sas.com/t5/user/viewprofilepage/user-id/15363"&gt;@SteveDenham&lt;/a&gt;&amp;nbsp;&amp;nbsp;&lt;a href="https://communities.sas.com/t5/user/viewprofilepage/user-id/13758"&gt;@lvm&lt;/a&gt;&amp;nbsp;&lt;/P&gt;
&lt;P&gt;&amp;nbsp;&lt;/P&gt;</description>
      <pubDate>Sat, 05 Feb 2022 08:00:19 GMT</pubDate>
      <guid>https://communities.sas.com/t5/SAS-Programming/Help-with-longitudinal-overdispersed-count-data-via-PROC-GLIMMIX/m-p/794657#M254807</guid>
      <dc:creator>Ksharp</dc:creator>
      <dc:date>2022-02-05T08:00:19Z</dc:date>
    </item>
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