{"id":37187,"date":"2013-06-09T11:44:38","date_gmt":"2013-06-09T15:44:38","guid":{"rendered":"http:\/\/1boringoldman.com\/?p=37187"},"modified":"2013-06-09T22:23:38","modified_gmt":"2013-06-10T02:23:38","slug":"food-for-thought","status":"publish","type":"post","link":"https:\/\/1boringoldman.com\/index.php\/2013\/06\/09\/food-for-thought\/","title":{"rendered":"food for thought"},"content":{"rendered":"
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Decline in placebo-controlled trial results suggests new directions for comparative effectiveness research<\/a>.
by Olfson M and Marcus SC.<\/strong><\/sup>
Health Affairs<\/font><\/strong>. 2013 32[6]:1116-1125.<\/div>\n

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The Affordable Care Act offers strong support for comparative effectiveness research, which entails comparisons among active treatments, to provide the foundation for evidence-based practice. Traditionally, a key form of research into the effectiveness of therapeutic treatments has been placebo-controlled trials, in which a specified treatment is compared to placebo. These trials feature high-contrast comparisons between treatments. Historical trends in placebo-controlled trials have been evaluated to help guide the comparative effectiveness research agenda. We investigated placebo-controlled trials reported in four leading medical journals between 1966 and 2010. We found that there was a significant decline in average effect size or average difference in efficacy [the ability to produce a desired effect] between the active treatment and placebo. On average, recently studied treatments offered only small benefits in efficacy over placebo. A decline in effect sizes in conventional placebo-controlled trials supports an increased emphasis on other avenues of research, including comparative studies on the safety, tolerability, and cost of treatments with established efficacy.<\/sup><\/strong><\/div>\n<\/blockquote>\n
I’ve kind of developed a rule without thinking about it that people with hundreds of published articles must be dangerous characters. But I recognized Dr. Olfson’s name and found four of his articles on this blog previously quoted that I thought were well-done and right-thinking and PubMed lists him at 300 articles. So maybe my unconscious rule has exceptions:<\/strong><\/sup><\/div>\n
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    National trends in the outpatient diagnosis and treatment of bipolar disorder in youth<\/a>. Archives of General Psychiatry<\/font><\/strong>. 2007 Sep;64[9]:1032-1039.<\/div>\n<\/li>\n

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    In this study, they plotted effect size of drug\/placebo differences in randomly chosen clinical trials from four major journals [JAMA<\/font><\/strong>, BMJ<\/font><\/strong>, NEJM<\/font><\/strong>, Lancet<\/font><\/strong>] going back to the time the trials became required. These are not just psychiatry articles, they cover all disease areas. Here’s the breakdown of their data set:<\/div>\n
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    This is their prime finding – the fall in effect size of the clinical trials over their study period: <\/div>\n
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    I graphed some of their tabular data to make it easier to see [all were significant]. On the left is the percent of Clinical Trials where PHARMA funding was acknowledged. This figure is confusing as that only became a requirement during the study period [NEJM<\/font><\/strong> 1985, JAMA<\/font><\/strong> 1989, BMJ<\/font><\/strong> 1994, Lancet<\/font><\/strong> 1994]. On the right, the number of authors rose from a median of 2-3 to 8-20 over the study period! Who knew?<\/p>\n

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    The left graph below shows the median number of sites used in the clinical trials. The trend to multiple sites is a striking phenomenon of the recent past, I presume reflecting the ascendency of of the Clinical Research Organizations. Similarly, in the center the median number of study subjects has soared over the last two decades [chasing significance]. And on the right, another way of showing the falling effect size – the mean odds ratio.<\/p>\n

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    And finally some miscellaneous stats from their tables. In this study the % of non-US studies, studies with insignificant Primary Outcomes, and drop-out rates did not change significantly. Intent-to-treat analysis means all subjects that start the study are included including drop-outs etc [corrected for missing data]. Studies reporting number of subjects screened and ITT analysis increased significantly.<\/p>\n


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    The article has an excellent but long discussion of the why of all of this, and concludes:<\/div>\n