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From |
Ronan Conroy <rconroy@rcsi.ie> |

To |
statalist@hsphsun2.harvard.edu |

Subject |
Re: st: RE: Quantile question |

Date |
Mon, 3 Mar 2008 11:14:58 +0000 |

On 2 Mar 2008, at 17:01, Nick Cox wrote:

Sorry if that wasn't clear. Perhaps an example would help. I was involved in looking at the effect of a number of measures of the patient's response to illness as predictors of depression - work and social adjustment, symptom frequency, symptom bother, and so on. These were measured using standardised scales, but each scale had its own theoretical range, its own empirical score distribution and, most important, each scale was measured in arbitrary units.

I don't understand what you understand by quantile here. In particular,

what is a "1-quantile" increase?

It was useful for the reader to be able to see the odds ratios (and confidence intervals) associated with a 1-decile increase in each of these predictors, as it gave them a way of comparing their effects and of judging their practical importance as well as their statistical significance.

There are times, then, when quantiles do not lose information but increase it, by converting unfamiliar and arbitrary measurement scales to a definable measurement unit.

P Before printing, think about the environment

=================================

Ronan Conroy

rconroy@rcsi.ie

Royal College of Surgeons in Ireland

Epidemiology Department,

120 St Stephen's Green, Dublin 2, Ireland

+353 (0)1 402 2431

+353 (0)87 799 97 95

http://www.flickr.com/photos/ronanconroy/sets/72157601895416740/

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**References**:**RE: st: RE: Quantile question***From:*"Nick Cox" <n.j.cox@durham.ac.uk>

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