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Re: st: semiparametric mixture cure models


From   "E. Paul Wileyto" <epw@mail.med.upenn.edu>
To   statalist@hsphsun2.harvard.edu
Subject   Re: st: semiparametric mixture cure models
Date   Tue, 23 Feb 2010 13:31:45 -0500

My homegrown models are pretty much equivalent to Paul Lambert's models. His stuff has error handling, mine doesn't.

By 0.4, I assume you mean 40%. That 1.2% estimate may have to do with your data still declining at censoring time. That parametric cure model is fitted to the shape of the uncensored data, and if at censoring there's a long way to go, and it is still declining (even slowly), it may well project that the floor is far below the current level. It doesn't surprise me.

P

Sridhar Telidevara wrote:
Paul, Thank you for your observations and suggestions. I will estimate
the models using strsnmix, and frailty models.


The floor on the Kaplan Meier estimate is 0.4. I have used accelerated
failure time model, loglogistic, weibull and lognormal distributions
to model the latency part and logit link function for the cure
fraction. loglogistic distribution appears to be a best fit (of all
the three) at the top of the KM curve but not towards the tail. The
estimated marginal survivor function appears to taper off, but below
the floor of the KM estimate, around 0.37. The estimated cure fraction
fraction from the model is around 1.2%. Median survival time is around
1500 days. Marginal survivor function of a simple loglogistic model
without cure did not approximate the KM curve well and it was above
the KM curve all throughout the observation period.

Do you think your home-grown models will be of any help?

Thanks,

Sridhar




On Tue, Feb 23, 2010 at 10:34 AM, E. Paul Wileyto
<epw@mail.med.upenn.edu> wrote:
If you are talking about a Cox-type regression on top of a cure mixture,
then no.  These have real identifiability problems, and identification
usually involves making an assumption about where the floor is.

There is strsnmix, Paul Lambert's parametric cure-mixture model, and it
works well.  I have some home-grown stuff, but it is still parametric.  If
you have trouble with identification using the parametric model, it may be
because the floor is vague in your data.  Parametric cure models fit well if
you can actually see the floor in a Kaplan Meier plot.  If you do not see
the survivorship leveling off in the KM plot, the floor of the cure model
doesn't have any way to ID the floor in your data.

Have you thought of modeling with frailty?  You may achieve the same kind of
result with a slightly different set of assumptions.

P



Sridhar Telidevara wrote:
Are there any stata routines for estimating a semiparametric mixture cure
model with fixed covariates?  The data have 30,000  observations and the
duration of observation is in days ( 1 day to 1096 days).

I have estimated several parametric mixture cure models and they did not
yield good results. Cure percentage was very low, the fit had long tails
and
the median survival time is greater than 1096 days. Therefore, there
exists
an identification problem.


Further,

1.       since the data is in days, is stset enough or episode splitting
is
required?


2.       Convergence problems with the EM algorithm and the bootstrapping
procedures if the data is in days.


3.       If I have to group the data to estimate a semiparametric mixture
cure model what is the optimal way to group the data? 5 days, 10 days? 30
days?


Thank you for your help,


Sridhar Telidevara

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--
E. Paul Wileyto, Ph.D.
Assistant Professor of Biostatistics
Tobacco Use Research Center
School of Medicine, U. of Pennsylvania
3535 Market Street, Suite 4100
Philadelphia, PA  19104-3309

215-746-7147
Fax: 215-746-7140
epw@mail.med.upenn.edu
*
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*   http://www.stata.com/help.cgi?search
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*   http://www.ats.ucla.edu/stat/stata/


--
E. Paul Wileyto, Ph.D.
Assistant Professor of Biostatistics
Tobacco Use Research Center
School of Medicine, U. of Pennsylvania
3535 Market Street, Suite 4100
Philadelphia, PA  19104-3309

215-746-7147
Fax: 215-746-7140
epw@mail.med.upenn.edu
*
*   For searches and help try:
*   http://www.stata.com/help.cgi?search
*   http://www.stata.com/support/statalist/faq
*   http://www.ats.ucla.edu/stat/stata/


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