On 6/14/2013 6:28 AM, Weyant, Robert J wrote:
> I am looking for input on what would be considered a "minimum set"
> of statistical concepts to be taught in an intro Evidence-based
> Health Care Course to first professional health care students
> (physicians, nurses, dentists, etc.). My experience has been that we
> teach "too much" stats, and turn off people. So I am curious what
> others consider to be essential statistical knowledge for competency
> in EBHC.
You could argue that all of the concepts associated with the critical
appraisal step are statistical in nature, but let's exclude those
concepts that are associated with the design of an study (randomization,
blinding, surrogate outcomes, etc.) and focus on concepts associated
with data analysis.
My list of essential statistical concepts were described in Chapter 6
(What do all these numbers mean?) of my book about Evidence Based
Medicine (Statistical Evidence in Medical Trials). Here's the material
that I covered:
--> Samples and populations
--> Type I and II errors
--> Confidence intervals
--> P-values
--> Odds ratio, relative risk (and NNT)
--> Correlation
--> Survival curves
--> Prevalence and incidence
With the exception of samples and populations, these are listed roughly
in priority order.
If I were writing the book again, I might add a bit about linear
regression to the material on correlation. I was told to leave out
diagnostic tests, so I did not include sensitivity, specificity,
likelihood ratios, etc.
I also have a chapter on Systematic Overviews/Meta-analysis, and I
introduce three other statistical concepts in that chapter:
--> the Forest Plot, and
--> the Funnel Plot.
--> Cochran's Q (and I-squared),
Again these are listed in priority. If you had to understand one concept
in meta-analysis, it would be how to read and interpret a forest plot.
I agree with the comment of Dr. Ogston that we want to create consumers
of statistics. The turn-off is not Statistics, per se, but the belief
among many Statistics teachers that you have to know enough to be able
to produce these statistics yourself.
Also, there is a strong aversion in your audience to formulas. It
shouldn't be that way. Formulas are our friends and allow us to express
in a single line what otherwise might take hundreds of words to
describe. But it is what it is. Most people hate formulas. The only
blessing for me is that all the aversion to formulas means that I can
charge slightly more than the minimum wage for my consulting projects.
It is possible to teach Statistics without formulas. The formulas become
very important for producers of statistics, but consumers don't need the
formulas.
Here are topics that I would stay away from:
--> t-tests
--> ANOVA
--> Non-parametric tests
--> the normal distribution
--> probability concepts
These are all excellent topics, but tend to be heavy on formulas and are
often of more interest to those who want to run their own studies and
produce their own statistics.
If this list of essentials that I gave above is too long, I'd pare it
back to just confidence intervals and p-values (which implies at least
some discussion of Type I/II errors). If you don't understand confidence
intervals and p-values (and appreciate all the misinterpretations that
are common for these concepts) then you can't really read the primary
studies effectively. The next most important thing would be odds ratios
(and relative risk and NNT).
Steve Simon, [log in to unmask], Standard Disclaimer.
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