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st: re: Non-parametric logistic regression


From   "Heather Gold" <heg2001@med.cornell.edu>
To   statalist@hsphsun2.harvard.edu
Subject   st: re: Non-parametric logistic regression
Date   Thu, 19 Sep 2002 10:05:14 -0400


>Is there in Stata a non-parametric regression procedure that allow
>non-parametric curve fitting of dose-response relationships with adjustment
>(parametrically or not) for covariates. The intent is to perform a
>non-parametric logistic regression analysis.

I believe if you interact all the covariates with each other, you will get a nonparametric logistic regression. For variables you want as proportional hazards (or parametric), _don't_ interact them with other variables.

eg, logit exit treat T2-T12 treat_T2-treat_T12 reg1 reg3 age2 age3 marital race2 race3;

The outcome (response) variable is "exit", the treatment (dose) variable is "treat", T2-T12 are duration indicators (time-from-diagnosis=duration) which are then interacted with "treat" to get a fully-interacted baseline hazard, and all the other covariates in the model are assumed to act proportionately.

Another way is to use the "if" option and only run the model conditional on a particular treatment (or dose), say. This creates a fully-interacted baseline hazard model without creating all the interacted covariates. But, this does not allow you to formally test for differences across treatments (doses) except perhaps by bootstrapping.

eg, logit exit T2-T12 reg1 reg3 age2 age3 marital race2 race3 if treat==1;
logit exit T2-T12 reg1 reg3 age2 age3 marital race2 race3 if treat==0;

The outcome (response) variable is "exit", and the treatment (dose) variable is "treat". In this way "treat" is fully interacted with all the covariates.

Remember if you have more than one observation per individual, then you'll want to cluster on ID.

Others might have easier solutions. I hope this is helpful.

Best,
Heather

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