<?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: trend analysis in SAS Procedures</title>
    <link>https://communities.sas.com/t5/SAS-Procedures/trend-analysis/m-p/465468#M70557</link>
    <description>&lt;P&gt;Since all your independent variables are CLASS variables, then your data indeed like a contingency table.&lt;/P&gt;
&lt;P&gt;Check PROC FREQ + TEAND option. there is an example of it in PROC FREQ's documentation.&lt;/P&gt;</description>
    <pubDate>Mon, 28 May 2018 13:07:30 GMT</pubDate>
    <dc:creator>Ksharp</dc:creator>
    <dc:date>2018-05-28T13:07:30Z</dc:date>
    <item>
      <title>trend analysis</title>
      <link>https://communities.sas.com/t5/SAS-Procedures/trend-analysis/m-p/465297#M70546</link>
      <description>&lt;P&gt;I am trying to see whether there is a significant upward or downward trend in certain infection over time. &amp;nbsp;I calculated rates/95% confidence intervals using proc genmod. &amp;nbsp;Can I use Cochran-armitage test (year vs. mean proportion) for trends? &amp;nbsp;If not, what method would be appropriate?&lt;/P&gt;</description>
      <pubDate>Sun, 27 May 2018 02:12:40 GMT</pubDate>
      <guid>https://communities.sas.com/t5/SAS-Procedures/trend-analysis/m-p/465297#M70546</guid>
      <dc:creator>docfermi</dc:creator>
      <dc:date>2018-05-27T02:12:40Z</dc:date>
    </item>
    <item>
      <title>Re: trend analysis</title>
      <link>https://communities.sas.com/t5/SAS-Procedures/trend-analysis/m-p/465323#M70550</link>
      <description>&lt;P&gt;Does your data like Contingency Table ? If it was ,check PROC FREQ + TREND option.&lt;/P&gt;
&lt;P&gt;Or try ANOVA via PROC GLM .&lt;/P&gt;</description>
      <pubDate>Sun, 27 May 2018 10:39:27 GMT</pubDate>
      <guid>https://communities.sas.com/t5/SAS-Procedures/trend-analysis/m-p/465323#M70550</guid>
      <dc:creator>Ksharp</dc:creator>
      <dc:date>2018-05-27T10:39:27Z</dc:date>
    </item>
    <item>
      <title>Re: trend analysis</title>
      <link>https://communities.sas.com/t5/SAS-Procedures/trend-analysis/m-p/465362#M70553</link>
      <description>&lt;P&gt;I got yearly rates with below:&lt;/P&gt;&lt;P&gt;&amp;nbsp;&lt;/P&gt;&lt;P&gt;proc genmod data=cdi;&lt;/P&gt;&lt;P&gt;&amp;nbsp;class year sex age_group / param=glm;&lt;/P&gt;&lt;P&gt;model cdiff= year sex age_group / type3 dist=poisson link=log offset=log_discharge;&lt;/P&gt;&lt;P&gt;estimate "rate: year=2003" intercept 1 year 1 0 0 0 0 /e&lt;/P&gt;&lt;P&gt;.........;run;&lt;/P&gt;&lt;P&gt;&amp;nbsp;&lt;/P&gt;&lt;P&gt;I would appreciate&amp;nbsp;sas codes or references for trend analyses.&lt;/P&gt;</description>
      <pubDate>Sun, 27 May 2018 21:12:19 GMT</pubDate>
      <guid>https://communities.sas.com/t5/SAS-Procedures/trend-analysis/m-p/465362#M70553</guid>
      <dc:creator>docfermi</dc:creator>
      <dc:date>2018-05-27T21:12:19Z</dc:date>
    </item>
    <item>
      <title>Re: trend analysis</title>
      <link>https://communities.sas.com/t5/SAS-Procedures/trend-analysis/m-p/465468#M70557</link>
      <description>&lt;P&gt;Since all your independent variables are CLASS variables, then your data indeed like a contingency table.&lt;/P&gt;
&lt;P&gt;Check PROC FREQ + TEAND option. there is an example of it in PROC FREQ's documentation.&lt;/P&gt;</description>
      <pubDate>Mon, 28 May 2018 13:07:30 GMT</pubDate>
      <guid>https://communities.sas.com/t5/SAS-Procedures/trend-analysis/m-p/465468#M70557</guid>
      <dc:creator>Ksharp</dc:creator>
      <dc:date>2018-05-28T13:07:30Z</dc:date>
    </item>
  </channel>
</rss>

