Category: Clinical importance. Clinical importance represents a change or shift in the outcome between the treatment group and the control group that is large enough to have a practical impact on the patient. Articles are arranged by date with the most recent entries at the top. You can find the theme and closely related categories and other resources at the bottom of this page.

Stats: What to measure in a post-marketing surveillance study (May 2, 2007). Dear Professor Mean, I am volunteering as a data analyst in a post-marketing surveillance to assess the safety and efficacy of a drug. I'm not sure what to measure and how to measure it. Can you help me figure out what really needs to be done?

Stats: Is my confidence interval too wide? (September 21, 2006). Dear Professor Mean, Is there a rule of the thumb to judge if a 95% CI is wide or narrow?

Stats: Confidence intervals are needed to evaluate clinical importance (December 15, 2005). Back in March, I sent a letter to the American Journal of Psychiatry complaining about their failure to include confidence intervals in their published reports. The journal decided not to publish this letter, but since it discusses an important general issue, I thought I would place the submitted letter here.

Stats: Do I have enough data after 24 months of time? (April 5, 2005). Someone asked me about a correlation coefficient that he computed on a data set that represented 24 months of data collection. A particular correlation of interest (a correlation between staff turnover and resident falls) was not significantly different from zero, but this person wanted to know how much more data to collect before safely concluding that no relation has been or likely will be established. First compute a confidence interval for the correlation coefficient. If that interval is so narrow that you can rule out the possibility of a clinically important shift, then your sample size is large enough.

Stats: Where is the confidence interval? (March 31, 2005). A recent letter to the editor in the American Journal of Psychiatry complains about an article claiming that a drug, citalopram, can reduce depressive symptoms. The letter writers dispute (among other things) the claim of a statistically and clinically significant reduction. In the original paper, the authors show several results, and the one that is perhaps the most important is the proportion of patients who score 28 or less on the Children's Depression Rating Scale. By this criteria, 36% of the treated patients and 24% of the control patients showed improvement. One way to see if the results of a study are clinically significant is to present a number needed to treat plus confidence limits.

Stats: Clinical importance (March 11, 2005). Many journal authors have the bad habit of looking just at the p-value of a study and ignoring the clinical importance of their findings. If they get a small p-value, which indicates a statistically significant difference between the new therapy and the standard therapy, they dance in the streets, they pop open the champagne bottles, they celebrate wildly, and they publish their results in an "A" journal. If they get a large p-value, they rend their clothes, they throw ashes on their heads, they wail and moan, and they publish their results in a "C" journal. An article about measurement of fatigue offers some valuable lessons about clinically relevant differences.

Stats: Clinically trivial effects (April 12, 2004). I don't like to cite articles in the New York Times, because they are free on the web only for a couple of weeks. But an article by Denise Grady, Nominal Benefits Seen in Drugs for Alzheimers, published on April 7 is worth mentioning. Grady writes that drugs to treat Alzheimer patients are expensive, and it is unclear how much they really help.

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This webpage was written by Steve Simon on 2007-06-04, edited by Steve Simon, and was last modified on 2008-07-08. Send feedback to ssimon at cmh dot edu or click on the email link at the top of the page.