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st: Re Lilian tesman- Predict mortality


From   Kay Walker <kay.walker@internode.on.net>
To   statalist@hsphsun2.harvard.edu
Subject   st: Re Lilian tesman- Predict mortality
Date   Sun, 18 Jul 2010 21:39:05 +0930

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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