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From |
"David Radwin" <[email protected]> |

To |
<[email protected]> |

Subject |
RE: st: Simplification of formula in logistic regression |

Date |
Mon, 16 May 2011 08:23:39 -0700 (PDT) |

Mikkel, On why not to group continuous variables, please see http://www.stata.com/statalist/archive/2011-03/msg00154.html and references therein, particularly: Wainer, H., Geseroli, M. & Verdi, M. 2006. Finding what is not there through the unfortunate binning of results: The Mendel effect. Chance, 19(1):49-56. http://www.amstat.org/publications/chance/2006/CHANCE%2019_1.pdf David -- David Radwin Research Associate MPR Associates, Inc. 2150 Shattuck Ave., Suite 800 Berkeley, CA 94704 Phone: 510-849-4942 Fax: 510-849-0794 www.mprinc.com > -----Original Message----- > From: [email protected] [mailto:owner- > [email protected]] On Behalf Of Nick Cox > Sent: Sunday, May 15, 2011 9:49 AM > To: [email protected] > Subject: Re: st: Simplification of formula in logistic regression > > Sorry, but I think you will continue find this "correct way" to be > elusive. > > Nick > > On Sun, May 15, 2011 at 4:23 PM, Mikkel Brabrand <[email protected]> > wrote: > > If I want clinicians to use my model, it needs to be simple. I cannot > expect them to use a piece of software to calculate the risk score and it > is virtually impossible to have it incorporated in the programs used at my > department. I therefore need to simplify it and make the variables > categorized or dichotomous. I have previously used the trial and error > way, and come up with a model that seems reasonable (and tested it in an > independent cohort, and am now testing it in two external cohorts at other > hospitals). However, there must be a correct way to select the cuf-off > levels, I just cannot find out how. I have asked most statisticians I have > met on my way, but no one seems to know how. I hoped that some of you > might have a suggestion... > > > > Mikkel > > > > Den 15/05/2011 kl. 16.49 skrev Nick Cox: > > > >> I don't know what "statistically correct" would mean here. If you > >> think your model is useful, there are no grounds for coarsening it. If > >> the implication is that clinicians can't understand or don't need to > >> understand the internals of the formula you can think of encapsulating > >> the details in a Stata do-file or some equivalent in other software. > >> > >> A broad issue is that detailed models optimised to fit particular > >> datasets often perform poorly on other data. > >> > >> Nick > >> > >> On Sun, May 15, 2011 at 3:43 PM, Mikkel Brabrand <[email protected]> > wrote: > >> > >>> I have performed a logistic regression analysis including five > variables and one outcome. However, I would like to simplify the formula > significantly for clinical use. So, instead of the formula been something > like -12.22+2.33*systolic blood pressure-1.21*temperature etc., I would > like to make a scoring system where the score is calculated on basis of > the measured values of the vital signs. > >>> > >>> An example could be something like this > >>> > >>> .................2 points..1 point...0 points...1 point.....2 points > >>> > >>> Pulse ...........-30........31-50....51-100....101-200..201- > >>> > >>> Sys. BP.........-60........61-100..101-200...201- > >>> > >>> However, I have no idea how to find the optimal cut-off points. Do any > of you have a suggestion how to do this statistically correct? > >> > >> * > > * > * For searches and help try: > * http://www.stata.com/help.cgi?search > * http://www.stata.com/support/statalist/faq > * http://www.ats.ucla.edu/stat/stata/ * * For searches and help try: * http://www.stata.com/help.cgi?search * http://www.stata.com/support/statalist/faq * http://www.ats.ucla.edu/stat/stata/

**References**:**st: Simplification of formula in logistic regression***From:*Mikkel Brabrand <[email protected]>

**Re: st: Simplification of formula in logistic regression***From:*Nick Cox <[email protected]>

**Re: st: Simplification of formula in logistic regression***From:*Mikkel Brabrand <[email protected]>

**Re: st: Simplification of formula in logistic regression***From:*Nick Cox <[email protected]>

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