<?xml version="1.0" encoding="UTF-8"?>
<rss xmlns:content="http://purl.org/rss/1.0/modules/content/" xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:rdf="http://www.w3.org/1999/02/22-rdf-syntax-ns#" xmlns:taxo="http://purl.org/rss/1.0/modules/taxonomy/" version="2.0">
  <channel>
    <title>topic Re: Great disparity in binomial regression odds ratio between proc genmod and proc logistic in Statistical Procedures</title>
    <link>https://communities.sas.com/t5/Statistical-Procedures/Great-disparity-in-binomial-regression-odds-ratio-between-proc/m-p/812458#M40062</link>
    <description>&lt;P&gt;It is because those are entirely different models - one a Generalized Estimating Equations logistic model using GEE (not maximum likelihood) estimation, and the other is a conditional logistic model that maximizes a conditional likelihood. Differences should be expected when fitting such different models using differing estimation algorithms. Importantly, the GEE model is a population-averaged model while the conditional logistic model is a subject-specific model. Yet another possible approach is the random-effects model as can be fit in PROC GLIMMIX which is also a subject-specific model. If you want to make inferences about population differences, then the population-averaged GEE model is more appropriate. If you want to make inferences about, say, change over time (if the repeated measures are over time), then a subject-specific approach can be used. This distinction in model types and estimation methods is discussed in many texts and papers. One paper available online is Hu et. al. (1998), "Comparison of Population-Averaged and Subject-Specific Approaches for Analyzing Repeated Binary Outcomes."&lt;/P&gt;
&lt;P&gt;&amp;nbsp;&lt;/P&gt;
&lt;P&gt;For these and other kinds of logistic models, see &lt;A href="http://support.sas.com/kb/22871" target="_self"&gt;this note&lt;/A&gt;.&lt;/P&gt;
&lt;P&gt;&amp;nbsp;&lt;/P&gt;</description>
    <pubDate>Tue, 10 May 2022 17:04:59 GMT</pubDate>
    <dc:creator>StatDave</dc:creator>
    <dc:date>2022-05-10T17:04:59Z</dc:date>
    <item>
      <title>Great disparity in binomial regression odds ratio between proc genmod and proc logistic</title>
      <link>https://communities.sas.com/t5/Statistical-Procedures/Great-disparity-in-binomial-regression-odds-ratio-between-proc/m-p/812439#M40058</link>
      <description>&lt;P&gt;Hello,&lt;/P&gt;
&lt;P&gt;&amp;nbsp;&lt;/P&gt;
&lt;P&gt;Please, I need help figuring out why there is a great disparity in the binomial regression odds ratio between proc genmod and proc logistic. I would also like to know the more reliable result.&lt;/P&gt;
&lt;P&gt;The data was obtained after matching. The following are my codes and results.&lt;/P&gt;
&lt;P&gt;&amp;nbsp;&lt;/P&gt;
&lt;P&gt;Thank you!&lt;/P&gt;
&lt;PRE&gt;&lt;CODE class=" language-sas"&gt;proc genmod data=COVID.COVID_TRAUMA_MATCHED2 descending;
class  subclass COVID_19_Positivity(DESC) ; 
model  In_hosp_mortality(event='2')= COVID_19_Positivity
/ dist=bin ;
 repeated sub=subclass /type=cs corrw covb printmle;
 lsmeans COVID_19_Positivity/ diff exp cl or;
run;


proc logistic data=COVID.COVID_TRAUMA_MATCHED2; 
CLASS COVID_19_Positivity(DESC); 
strata  subclass;
   model In_hosp_mortality(event='2')= COVID_19_Positivity;
  run;
&lt;/CODE&gt;&lt;/PRE&gt;
&lt;P&gt;The result from proc genmod&lt;/P&gt;
&lt;P&gt;&lt;span class="lia-inline-image-display-wrapper lia-image-align-inline" image-alt="UcheOkoro_0-1652197634677.png" style="width: 1125px;"&gt;&lt;img src="https://communities.sas.com/t5/image/serverpage/image-id/71270iF290520C5559C530/image-dimensions/1125x108?v=v2" width="1125" height="108" role="button" title="UcheOkoro_0-1652197634677.png" alt="UcheOkoro_0-1652197634677.png" /&gt;&lt;/span&gt;&lt;/P&gt;
&lt;P&gt;The results from proc logistic&lt;/P&gt;
&lt;P&gt;&lt;span class="lia-inline-image-display-wrapper lia-image-align-inline" image-alt="UcheOkoro_2-1652197698509.png" style="width: 400px;"&gt;&lt;img src="https://communities.sas.com/t5/image/serverpage/image-id/71272iF7D740DF79A160FD/image-size/medium?v=v2&amp;amp;px=400" role="button" title="UcheOkoro_2-1652197698509.png" alt="UcheOkoro_2-1652197698509.png" /&gt;&lt;/span&gt;&lt;/P&gt;
&lt;P&gt;&amp;nbsp;&lt;/P&gt;
&lt;P&gt;&amp;nbsp;&lt;/P&gt;</description>
      <pubDate>Tue, 10 May 2022 15:49:24 GMT</pubDate>
      <guid>https://communities.sas.com/t5/Statistical-Procedures/Great-disparity-in-binomial-regression-odds-ratio-between-proc/m-p/812439#M40058</guid>
      <dc:creator>UcheOkoro</dc:creator>
      <dc:date>2022-05-10T15:49:24Z</dc:date>
    </item>
    <item>
      <title>Re: Great disparity in binomial regression odds ratio between proc genmod and proc logistic</title>
      <link>https://communities.sas.com/t5/Statistical-Procedures/Great-disparity-in-binomial-regression-odds-ratio-between-proc/m-p/812458#M40062</link>
      <description>&lt;P&gt;It is because those are entirely different models - one a Generalized Estimating Equations logistic model using GEE (not maximum likelihood) estimation, and the other is a conditional logistic model that maximizes a conditional likelihood. Differences should be expected when fitting such different models using differing estimation algorithms. Importantly, the GEE model is a population-averaged model while the conditional logistic model is a subject-specific model. Yet another possible approach is the random-effects model as can be fit in PROC GLIMMIX which is also a subject-specific model. If you want to make inferences about population differences, then the population-averaged GEE model is more appropriate. If you want to make inferences about, say, change over time (if the repeated measures are over time), then a subject-specific approach can be used. This distinction in model types and estimation methods is discussed in many texts and papers. One paper available online is Hu et. al. (1998), "Comparison of Population-Averaged and Subject-Specific Approaches for Analyzing Repeated Binary Outcomes."&lt;/P&gt;
&lt;P&gt;&amp;nbsp;&lt;/P&gt;
&lt;P&gt;For these and other kinds of logistic models, see &lt;A href="http://support.sas.com/kb/22871" target="_self"&gt;this note&lt;/A&gt;.&lt;/P&gt;
&lt;P&gt;&amp;nbsp;&lt;/P&gt;</description>
      <pubDate>Tue, 10 May 2022 17:04:59 GMT</pubDate>
      <guid>https://communities.sas.com/t5/Statistical-Procedures/Great-disparity-in-binomial-regression-odds-ratio-between-proc/m-p/812458#M40062</guid>
      <dc:creator>StatDave</dc:creator>
      <dc:date>2022-05-10T17:04:59Z</dc:date>
    </item>
    <item>
      <title>Re: Great disparity in binomial regression odds ratio between proc genmod and proc logistic</title>
      <link>https://communities.sas.com/t5/Statistical-Procedures/Great-disparity-in-binomial-regression-odds-ratio-between-proc/m-p/812461#M40064</link>
      <description>&lt;P&gt;Thank you so much!&amp;nbsp;&lt;/P&gt;</description>
      <pubDate>Tue, 10 May 2022 17:10:40 GMT</pubDate>
      <guid>https://communities.sas.com/t5/Statistical-Procedures/Great-disparity-in-binomial-regression-odds-ratio-between-proc/m-p/812461#M40064</guid>
      <dc:creator>UcheOkoro</dc:creator>
      <dc:date>2022-05-10T17:10:40Z</dc:date>
    </item>
  </channel>
</rss>

