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RE: st: Re Lilian tesman- Predict mortality


From   lilian tesmann <lilian_tes@hotmail.com>
To   <statalist@hsphsun2.harvard.edu>
Subject   RE: st: Re Lilian tesman- Predict mortality
Date   Tue, 20 Jul 2010 09:59:04 +1030

Hi Kay,
 
Thanks for your suggestion. I will explore a discriminant analysis option. With polinomials it's not feasible at this stage since we only have administrative data, which means that our predictors are limited in number, susceptible to miscoding, lack any time reference and are only yes/no classifications.
 
Lilian


----------------------------------------
> Date: Sun, 18 Jul 2010 21:39:05 +0930
> From: kay.walker@internode.on.net
> To: statalist@hsphsun2.harvard.edu
> Subject: st: Re Lilian tesman- Predict mortality
>
> Have you created your model for finding predictors of death from
> variables collected from ONLY the ones who have died, or from others? I
> would make the model from the ones who died and look at the order of the
> strengths of the predictors from best downwards, adding them in or out
> Step wise. I would also look at different types of regression eg.
> various polynomials as there are conditions where a "symptom" can become
> worse, or actually seem to lessen before death- so you get a
> curved/wavelike sequence of measurements on that variable- the human
> manifestations of variables often don't work the same way that numbers
> do. At this stage you wouldn't be doing a logistic regression as you
> only have one end point- death. The timing or order of variables may be
> important in real life as well, which might force you to have early/late
> versions of variables, eg. temperature can be high at night, normal
> during the day; heart rate can be fast during the acute phase , then
> slow down in those becoming well, but rise again in those who are going
> to die.
> After developing the best fit- or a selection of possibles, enter the
> measurements from ones who haven't died into the logistic model to see
> if there is a discernible pattern anything like the deceased ones.
> Depending on your data you might be better off doing a discriminant
> analysis on the dead vs. live and getting predictors from the variables
> which separate the groups best.
> I've only done this sort of modeling on diseases that are rare- like
> Duchennes Muscular Dystrophy- NOT on large population groups- so I might
> have given you a pile of garbage!.
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