# Statistical Errors in Medical Studies(2)

I have written about statistics [1], and various traps people often fall into when examining data before ( Statistics Insights for Scientists and Engineers [2], Data Can’t Lie – But People Can be Fooled [3], Correlation is Not Causation [4], Simpson’s Paradox [5]). And also have posted about reasons for systemic reasons for medical studies presenting misleading results ( Why Most Published Research Findings Are False [6], How to Deal with False Research Findings [7], Medical Study Integrity (or Lack Thereof) [8], Surprising New Diabetes Data [9]). This post collects some discussion on the topic from several blogs and studies.

HIV Vaccines, p values, and Proof [10] by David Rind

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So, the modestly positive result found in the trial must be weighed against our prior belief that such a vaccine would fail. Had the vaccine been dramatically protective, giving us much stronger evidence of efficacy, our prior doubts would be more likely to give way in the face of high quality evidence of benefit.

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While the actual analysis the investigators decided to make primary would be completely appropriate had it been specified up front, it now suffers under the concern of showing marginal significance after three bites at the statistical apple; these three bites have to adversely affect our belief in the importance of that p value. And, it’s not so obvious why they would have reported this result rather than excluding those 7 patients from the per protocol analysis and making that the primary analysis; there might have been yet a fourth analysis that could have been reported had it shown that all important p value below 0.05.

How to Avoid Commonly Encountered Limitations of Published Clinical Trials [11] by Sanjay Kaul, MD and and George A. Diamond, MD

Why Most Published Research Findings Are False [12] by John P. A. Ioannidis

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a research finding is less likely to be true when the studies conducted in a field are smaller; when effect sizes are smaller; when there is a greater number and lesser preselection of tested relationships; where there is greater flexibility in designs, definitions, outcomes, and analytical modes; when there is greater financial and other interest and prejudice; and when more teams are involved in a scientific field in chase of statistical significance.

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A finding from a well-conducted, adequately powered randomized controlled trial starting with a 50% pre-study chance that the intervention is effective is eventually true about 85% of the time.

We’re so good at medical studies that most of them are wrong [13] by John Timmer

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even the same factor can be accounted for using different mathematical means. The models also make decisions on how best handle things like measuring exposures or health outcomes. The net result is that two models can be fed an identical dataset, and still produce a different answer.

Odds are, it’s wrong [14] by Tom Siegfried

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“Determining the best treatment for a particular patient is fundamentally different from determining which treatment is best on average,” physicians David Kent and Rodney Hayward wrote in American Scientist in 2007. “Reporting a single number gives the misleading impression that the treatment-effect is a property of the drug rather than of the interaction between the drug and the complex risk-benefit profile of a particular group of patients.”

Related: Bigger Impact: 15 to 18 mpg or 50 to 100 mpg? [15] – Meaningful debates need clear information [16] – Seeing Patterns Where None Exists [17] – Fooled by Randomness [18] – Poor Reporting and Unfounded Implications [19] – Illusion of Explanatory Depth [20] – Mistakes in Experimental Design and Interpretation [21]

SOURCE [22]