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 outside resources at the bottom of
this page. Other entries about clinical importance can be found in the
clinical importance page at the
StATS website.
2008
[[There is no material yet from my new site.]]
Outside resources:
- Reliable and clinically
significant change. Chris Evans. Excerpt: Reliable Change (RC) is about
whether people changed sufficiently that the change is unlikely to be due to
simple measurement unreliability. You determine who has changed reliably (i.e.
more than the unreliability of the measure would suggest might happen for 95%
of subjects) by seeing if the difference between the follow-up and initial
scores is more than a certain level. That level is a function of the initial
standard deviation of the measure and its reliability. www.psyctc.org/stats/rcsc.htm
All of the material above this paragraph is licensed under a
Creative Commons Attribution 3.0 United States License. This page was written by
Steve Simon and was last modified on
2010-04-11. The material
below this paragraph links to my
old website, StATS. Although I wrote all of the material
listed below, my ex-employer, Children's Mercy Hospital, has claimed copyright
ownership of this material. The brief excerpts shown here are included under
the fair use provisions of U.S. Copyright laws.
2008
2007
- 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?
2006
- 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?
2005
- 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.
2004
- 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.
Closely related categories:
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entries
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This work is licensed under a
Creative
Commons Attribution 3.0 United States License. This page was written by
Steve Simon and was last modified on
2010-04-11.
