Re: st: Simplification of formula in logistic regression

[Nick Cox <njcoxstata@gmail.com>]

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 <mikkel@brabrand.net> 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 <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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