Re: st: Simplification of formula in logistic regression

[Mikkel Brabrand <mikkel@brabrand.net>]

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 <mikkel@brabrand.net> 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?
> 
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