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    <title>topic Re: how to build a GEE model (which confounders to include) in Statistical Procedures</title>
    <link>https://communities.sas.com/t5/Statistical-Procedures/how-to-build-a-GEE-model-which-confounders-to-include/m-p/647059#M31058</link>
    <description>&lt;P&gt;Using p values to decide isn't a good idea for a lot of reasons.&amp;nbsp; See the literature out there regarding variable selection methods, especially this one from Peter Flom:&lt;/P&gt;
&lt;P&gt;&lt;A href="http://denversug.org/presentations/2010coday/stopsteppresntn.pdf" target="_self"&gt;http://denversug.org/presentations/2010coday/stopsteppresntn.pdf&lt;/A&gt;&amp;nbsp; and this one from Peter Flom and David Cassell&amp;nbsp;&lt;A href="https://www.lexjansen.com/pnwsug/2008/DavidCassell-StoppingStepwise.pdf" target="_self"&gt;https://www.lexjansen.com/pnwsug/2008/DavidCassell-StoppingStepwise.pdf&lt;/A&gt;&amp;nbsp;.&amp;nbsp; So given all of that, my favorite method of variable selection is to consider what question you wish to answer.&amp;nbsp; That will drive the selection with greater validity than p value dredging.&amp;nbsp; If after that you are still faced with too many variables, you may want to consider some sort of LASSO,&amp;nbsp; LAR or elastic net based selection.&amp;nbsp; Check out PROC GLMSELECT and PROC HPGENSELECT.&amp;nbsp; You would need to force your repeated measure into the model, but then select other variables based on the criterion chosen.&amp;nbsp; The results could be saved and then you could explore more fully in PROC GEE.&lt;/P&gt;
&lt;P&gt;&amp;nbsp;&lt;/P&gt;
&lt;P&gt;Just a suggestion - I don't want to say that this is the "best" method.&lt;/P&gt;
&lt;P&gt;&amp;nbsp;&lt;/P&gt;
&lt;P&gt;SteveDenham&lt;/P&gt;</description>
    <pubDate>Tue, 12 May 2020 11:27:03 GMT</pubDate>
    <dc:creator>SteveDenham</dc:creator>
    <dc:date>2020-05-12T11:27:03Z</dc:date>
    <item>
      <title>how to build a GEE model (which confounders to include)</title>
      <link>https://communities.sas.com/t5/Statistical-Procedures/how-to-build-a-GEE-model-which-confounders-to-include/m-p/642833#M31040</link>
      <description>&lt;P&gt;Hello,&lt;/P&gt;&lt;P&gt;I have more a theory-based question over a SAS coding question. I am working with a longitudinal dataset, and beforehand, I performed some descriptive statistics to look at differences that existed between my three exposure groups. Then, I used a DAG and previous literature to help me see what types of confounders could exist between my exposure and my outcome.&amp;nbsp;&lt;/P&gt;&lt;P&gt;my question: when building my final GEE model to use, I want to know which confounders I need to include in the model. Do i need to perform univariate GEE models and if the confounder has a p-value less than .2, it should be included? Or do I use the confounders that were statistically different between my exposure groups when I performed the descriptive statistics? Basically, I want to know how I should decide which variables to include and which variables to leave out in my finalized GEE model.&lt;/P&gt;&lt;P&gt;&amp;nbsp;&lt;/P&gt;&lt;P&gt;thank you for your help!&lt;BR /&gt;&lt;BR /&gt;&lt;/P&gt;</description>
      <pubDate>Sat, 25 Apr 2020 06:00:35 GMT</pubDate>
      <guid>https://communities.sas.com/t5/Statistical-Procedures/how-to-build-a-GEE-model-which-confounders-to-include/m-p/642833#M31040</guid>
      <dc:creator>katy-barry</dc:creator>
      <dc:date>2020-04-25T06:00:35Z</dc:date>
    </item>
    <item>
      <title>Re: how to build a GEE model (which confounders to include)</title>
      <link>https://communities.sas.com/t5/Statistical-Procedures/how-to-build-a-GEE-model-which-confounders-to-include/m-p/647059#M31058</link>
      <description>&lt;P&gt;Using p values to decide isn't a good idea for a lot of reasons.&amp;nbsp; See the literature out there regarding variable selection methods, especially this one from Peter Flom:&lt;/P&gt;
&lt;P&gt;&lt;A href="http://denversug.org/presentations/2010coday/stopsteppresntn.pdf" target="_self"&gt;http://denversug.org/presentations/2010coday/stopsteppresntn.pdf&lt;/A&gt;&amp;nbsp; and this one from Peter Flom and David Cassell&amp;nbsp;&lt;A href="https://www.lexjansen.com/pnwsug/2008/DavidCassell-StoppingStepwise.pdf" target="_self"&gt;https://www.lexjansen.com/pnwsug/2008/DavidCassell-StoppingStepwise.pdf&lt;/A&gt;&amp;nbsp;.&amp;nbsp; So given all of that, my favorite method of variable selection is to consider what question you wish to answer.&amp;nbsp; That will drive the selection with greater validity than p value dredging.&amp;nbsp; If after that you are still faced with too many variables, you may want to consider some sort of LASSO,&amp;nbsp; LAR or elastic net based selection.&amp;nbsp; Check out PROC GLMSELECT and PROC HPGENSELECT.&amp;nbsp; You would need to force your repeated measure into the model, but then select other variables based on the criterion chosen.&amp;nbsp; The results could be saved and then you could explore more fully in PROC GEE.&lt;/P&gt;
&lt;P&gt;&amp;nbsp;&lt;/P&gt;
&lt;P&gt;Just a suggestion - I don't want to say that this is the "best" method.&lt;/P&gt;
&lt;P&gt;&amp;nbsp;&lt;/P&gt;
&lt;P&gt;SteveDenham&lt;/P&gt;</description>
      <pubDate>Tue, 12 May 2020 11:27:03 GMT</pubDate>
      <guid>https://communities.sas.com/t5/Statistical-Procedures/how-to-build-a-GEE-model-which-confounders-to-include/m-p/647059#M31058</guid>
      <dc:creator>SteveDenham</dc:creator>
      <dc:date>2020-05-12T11:27:03Z</dc:date>
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