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    <title>topic retention comparison in SAS Health and Life Sciences</title>
    <link>https://communities.sas.com/t5/SAS-Health-and-Life-Sciences/retention-comparison/m-p/21557#M990</link>
    <description>&lt;HTML&gt;&lt;HEAD&gt;&lt;/HEAD&gt;&lt;BODY&gt;&lt;P&gt;I don't think that you can test that hypothesis with these data.&amp;nbsp; Since this is aggregate data, each data element can represent some fo the same and different people from the previous column.&amp;nbsp; For instance, drugB shows somewhere between 80 and 230 people were taking the drug on Week3 who were not taking it on week 2.&lt;/P&gt;&lt;P&gt;&lt;/P&gt;&lt;P&gt;With some severly simplifying assumptions, you could do a test, but I am not sure how much trust you could put into it.&amp;nbsp; &lt;/P&gt;&lt;P&gt;&lt;/P&gt;&lt;P&gt;If you forced the data to be monotonically non-increasing, you could assume that each person took the drug until they stopped and generate a duration for each person.&amp;nbsp; Then you would have a total of 910 records (100+810) and could do a test (e.g. Wilcoxon).&amp;nbsp; However, it appears that you have people going on and off and on the drugs from week to week and that assumption is likely unfounded.&amp;nbsp; &lt;/P&gt;&lt;P&gt;&lt;/P&gt;&lt;P&gt;Alternatively, you could assume that the likelihood of taking a drug on any given week is independent of any other week (within a person) and apply a time series analysis.&amp;nbsp; That assumption is generally false in the medication adherence studies.&lt;/P&gt;&lt;P&gt;&lt;/P&gt;&lt;P&gt;Doc Muhlbaier&lt;/P&gt;&lt;P&gt;Duke&lt;/P&gt;&lt;/BODY&gt;&lt;/HTML&gt;</description>
    <pubDate>Fri, 30 Sep 2011 16:41:40 GMT</pubDate>
    <dc:creator>Doc_Duke</dc:creator>
    <dc:date>2011-09-30T16:41:40Z</dc:date>
    <item>
      <title>retention comparison</title>
      <link>https://communities.sas.com/t5/SAS-Health-and-Life-Sciences/retention-comparison/m-p/21556#M989</link>
      <description>&lt;HTML&gt;&lt;HEAD&gt;&lt;/HEAD&gt;&lt;BODY&gt;&lt;P&gt;how to compare the retention of 2 drugs, data is like:&lt;/P&gt;&lt;P&gt;&lt;/P&gt;&lt;P&gt;Drug&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp; week1&amp;nbsp;&amp;nbsp;&amp;nbsp; week2&amp;nbsp;&amp;nbsp;&amp;nbsp; week3 ...............................week20&lt;/P&gt;&lt;P&gt;drugA&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp; 100&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp; 82&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp; 55&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp; 26&lt;/P&gt;&lt;P&gt;drugB&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp; 810&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp; 580&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp; 460&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp; 312&amp;nbsp; &lt;/P&gt;&lt;P&gt;&lt;/P&gt;&lt;P&gt;The numbers are the patient count who keep using the the drug starting week1.&lt;/P&gt;&lt;P&gt;&lt;/P&gt;&lt;P&gt;question: retention statistically significant?&lt;/P&gt;&lt;P&gt;&lt;/P&gt;&lt;P&gt;Thanks.&lt;/P&gt;&lt;/BODY&gt;&lt;/HTML&gt;</description>
      <pubDate>Fri, 30 Sep 2011 16:14:11 GMT</pubDate>
      <guid>https://communities.sas.com/t5/SAS-Health-and-Life-Sciences/retention-comparison/m-p/21556#M989</guid>
      <dc:creator>skk_2011</dc:creator>
      <dc:date>2011-09-30T16:14:11Z</dc:date>
    </item>
    <item>
      <title>retention comparison</title>
      <link>https://communities.sas.com/t5/SAS-Health-and-Life-Sciences/retention-comparison/m-p/21557#M990</link>
      <description>&lt;HTML&gt;&lt;HEAD&gt;&lt;/HEAD&gt;&lt;BODY&gt;&lt;P&gt;I don't think that you can test that hypothesis with these data.&amp;nbsp; Since this is aggregate data, each data element can represent some fo the same and different people from the previous column.&amp;nbsp; For instance, drugB shows somewhere between 80 and 230 people were taking the drug on Week3 who were not taking it on week 2.&lt;/P&gt;&lt;P&gt;&lt;/P&gt;&lt;P&gt;With some severly simplifying assumptions, you could do a test, but I am not sure how much trust you could put into it.&amp;nbsp; &lt;/P&gt;&lt;P&gt;&lt;/P&gt;&lt;P&gt;If you forced the data to be monotonically non-increasing, you could assume that each person took the drug until they stopped and generate a duration for each person.&amp;nbsp; Then you would have a total of 910 records (100+810) and could do a test (e.g. Wilcoxon).&amp;nbsp; However, it appears that you have people going on and off and on the drugs from week to week and that assumption is likely unfounded.&amp;nbsp; &lt;/P&gt;&lt;P&gt;&lt;/P&gt;&lt;P&gt;Alternatively, you could assume that the likelihood of taking a drug on any given week is independent of any other week (within a person) and apply a time series analysis.&amp;nbsp; That assumption is generally false in the medication adherence studies.&lt;/P&gt;&lt;P&gt;&lt;/P&gt;&lt;P&gt;Doc Muhlbaier&lt;/P&gt;&lt;P&gt;Duke&lt;/P&gt;&lt;/BODY&gt;&lt;/HTML&gt;</description>
      <pubDate>Fri, 30 Sep 2011 16:41:40 GMT</pubDate>
      <guid>https://communities.sas.com/t5/SAS-Health-and-Life-Sciences/retention-comparison/m-p/21557#M990</guid>
      <dc:creator>Doc_Duke</dc:creator>
      <dc:date>2011-09-30T16:41:40Z</dc:date>
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