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RE: st: Power calculation for Beta/Odds Ratios in logistic regressionmodels

From   uri goldbourt <[email protected]>
To   [email protected]
Subject   RE: st: Power calculation for Beta/Odds Ratios in logistic regressionmodels
Date   Wed, 15 Aug 2007 15:29:39 -0400

Well and importantly said!


-----Original Message-----
From: [email protected]
[mailto:[email protected]] On Behalf Of Neil Shephard
Sent: Wednesday, August 15, 2007 9:08 AM
To: [email protected]
Subject: Re: st: Power calculation for Beta/Odds Ratios in logistic
regression models

On 8/15/07, Nico Hutter <[email protected]> wrote:
> Hi everyone,
> we would like to run a power calculation for beta-coefficients / odds
> ratios in logistic regression models with covariates.
> Is such a procedure implemented in SATA?
> Used command for the logistic regression model:
> svy: logit depression age sex comorbidity, or
> Data stems from a national representative survey. Dependent variable is
> depression. Comorbidity is dichotomised. Now, we are interested in the
> "post hoc" power of the odds ratio of comorbidity.
> Can anybody give us some advice, please? Thanks in advance!

Roger Newson has pointed to an appropriate solution, but I would
question why you wish to do this?

In my opinion "post hoc" power is a meaningless measurement that is
completely useless in interpreting results, and a practice that needs
to discouraged.

You have already done your test (logistic regression) and got your
results.  Knowing the 'power' your data had of detecting this size of
effect (or more likely lack of) will tell you nothing more informative
about the association.  Its like trying to tell someone who's just one
the lottery that they shouldn't buy lottery tickets because the chance
of winning is so low, they're not going to care as they've already got
their answer.

There are a few papers around that discuss this in greater detail (I'm
sure there are more).



Goodman SN, Berlin JA (1994) The Use of Predicted Confidence Intervals
when Planning Experiments and the Misuse of Power When Interpreting
Results. Annals of Internal Medicine 121.3:200-206

Hoenig J.M., Heisey D.M. (2001) The Abuse of Power: The Pervasive
Fallacy of Power Calculations for Data Analysis. The American
Statistician  55:19-24

Levine M, Ensom MH (2001) Post hoc power analysis: an idea whose time
has passed? Pharmacotherapy 21.4:405-409

"In mathematics you don't understand things. You just get used to
them."  - Johann von Neumann

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