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From |
Nico Hutter <Nico.Hutter@psychologie.uni-freiburg.de> |

To |
statalist@hsphsun2.harvard.edu |

Subject |
Re: st: Power calculation for Beta/Odds Ratios in logistic regressionmodels |

Date |
Thu, 16 Aug 2007 17:27:22 +0200 |

Thanks Roger and Thanks Neil for the very helpful comments to our question!

Indeed we had two intentions:

First, we wanted to use post hoc power analysis for a further interpretation of non-significant odds ratios in an existing study. This approach is useless, what we have learned from your notes and references.

Second, we also tried to calculate the sample size for a "hypothetical" new study on the basis of the data of the existing study. This still seems to be reasonable.

Best wishes

Nico

Newson, Roger B schrieb:

I would argue that it depends what you mean by "post hoc power

calculations".

If Nico is proposing to calculate the power to detect a POPULATION odds

ratio of the size of the observed SAMPLE odds ratio, then that would

indeed be misleading and uninformative. The Central Limit ?Theorem

implies that the power to detect a POPULATION odds ratio the size of the

observed SAMPLE odds ratio, using the observed SAMPLE P-value as the

threshold, will always be 0.5 (or 50 percent). Therefore, it will not be

surprising if the study was underpowered to detect a POPULATION odds

ratio as large as the observed SAMPLE odds ratio, even though the study

may have been adequately powered to detect a larger POPULATION odds

ratio, which was considered to be clinically interesting by the study

designers at the time of the study design.

HOWEVER, if Nico intends to use an existing study to inform the design

of a possible larger (and therefore more sensitive) study, using the

existing study as a pilot, then Nico is doing nothing wrong. In fact,

Nico is probably doing the best that anybody can do, given that the

proposed analysis method appears to be confounder-adjusted, implying

that doing power calculations from first principles will be very

complicated. And there may be a genuine lack of consensus regarding what

effect size is "clinically interesting".

I hope this helps.

Best wishes

Roger

Roger Newson

Lecturer in Medical Statistics

Respiratory Epidemiology and Public Health Group

National Heart and Lung Institute

Imperial College London

Royal Brompton campus

Room 33, Emmanuel Kaye Building

1B Manresa Road

London SW3 6LR

UNITED KINGDOM

Tel: +44 (0)20 7352 8121 ext 3381

Fax: +44 (0)20 7351 8322

Email: r.newson@imperial.ac.uk Web page: www.imperial.ac.uk/nhli/r.newson/

Departmental Web page:

http://www1.imperial.ac.uk/medicine/about/divisions/nhli/respiration/pop

genetics/reph/

Opinions expressed are those of the author, not of the institution.

-----Original Message-----

From: owner-statalist@hsphsun2.harvard.edu

[mailto:owner-statalist@hsphsun2.harvard.edu] On Behalf Of uri goldbourt

Sent: 15 August 2007 20:30

To: statalist@hsphsun2.harvard.edu

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

regression models

Well and importantly said!

UG

---------------------

-----Original Message-----

From: owner-statalist@hsphsun2.harvard.edu

[mailto:owner-statalist@hsphsun2.harvard.edu] On Behalf Of Neil Shephard

Sent: Wednesday, August 15, 2007 9:08 AM

To: statalist@hsphsun2.harvard.edu

Subject: Re: st: Power calculation for Beta/Odds Ratios in logistic

regression models

On 8/15/07, Nico Hutter <Nico.Hutter@psychologie.uni-freiburg.de> wrote:

Hi everyone,is

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

depression. Comorbidity is dichotomised. Now, we are interested in theRoger Newson has pointed to an appropriate solution, but I would

"post hoc" power of the odds ratio of comorbidity.

Can anybody give us some advice, please? Thanks in advance!

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).

Neil

References

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

--

Nico Hutter, Dipl. Psych.

Abt. für Rehabilitationspsychologie und Psychotherapie

Institut für Psychologie Universität Freiburg

D-79085 Freiburg

Tel: +49(0)761/203-9172

Fax: +49(0)761/203-3040

http://www.psychologie.uni-freiburg.de/Members/hutter

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**References**:**Re: st: Power calculation for Beta/Odds Ratios in logistic regression models***From:*"Neil Shephard" <nshephard@gmail.com>

**RE: st: Power calculation for Beta/Odds Ratios in logistic regressionmodels***From:*uri goldbourt <goldbu1@post.tau.ac.il>

**RE: st: Power calculation for Beta/Odds Ratios in logistic regression models***From:*"Newson, Roger B" <r.newson@imperial.ac.uk>

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