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RE: st: Frailty gamma or inv gaussian distribution


From   Alexander James <alexandre_lille-paris@hotmail.com>
To   <statalist@hsphsun2.harvard.edu>
Subject   RE: st: Frailty gamma or inv gaussian distribution
Date   Thu, 13 Sep 2012 05:28:02 -0300

Thanks Maarten,

You are right! I tested different distributions and for a weibull model there is not clear differences between a gamma or inverse gausian distribution.

Thanks again,

Alexandre

 
> Date: Wed, 12 Sep 2012 09:35:47 +0200
> Subject: Re: st: Frailty gamma or inv gaussian distribution
> From: maartenlbuis@gmail.com
> To: statalist@hsphsun2.harvard.edu
> 
> On Tue, Sep 11, 2012 at 9:25 PM, Alexander James wrote:
>> I am trying to run a survival model. I am facing an issue regarding how I should set the frailty. When I use gamma distribution I get very significant, but when I try to run it with inverter Gaussian distribution the level of significance of my main explanatory variable reduces substantially. I could not find any test to indicate which of them is more appropriate or a reference to explain why I am observing the chance in significance level. Would someone have a suggestion for this question?
> <snip>
>> ps. I am using an exponential model
> 
> Conceptually, a model with gamma frailty or inverse Gaussian frailty
> are very similar. So if they give very different results, than the
> conclusion is that your models aren't robust enough to draw meaningful
> conclusions. So rather than trying to select one of these models a the
> "best", you should try to make your model more robust(*).
> 
> In this case, I suspect your problem is the form of the baseline
> hazard that you have chosen by specifying an exponential model. Within
> an exponential model, you assume that the baseline hazard is constant
> over time. With frailty the baseline hazard will decline: the frail
> are selected out early, so at later points in time the more hardy
> remain. For more on that see: (Vaupel and Yashin 1985). If your model
> is wrong and the baseline hazard net of frailty is not constant over
> time, your model will try to assign all those changes in the baseline
> hazard to the frailty component, which can easily lead to an unstable
> model like the one you have. The solution is to try different baseline
> hazard functions, so try different distributions in the
> -distribution()- option of -streg- or try -stcox-.
> 
> Hope this helps,
> Maarten
> 
> (*) Despite the name, you are not going to achieve that by adding the
> -robust- or -vce(robust)- options: These refer to a completely
> different type of robustness.
> 
> James W. Vaupel and Anatoli I. Yashin (1985) Heterogeneity's Ruses:
> Some Surprising Effects of Selection on Population Dynamics. The
> American Statistician, 39(3): 176--185 .
> 
> ---------------------------------
> Maarten L. Buis
> WZB
> Reichpietschufer 50
> 10785 Berlin
> Germany
> 
> http://www.maartenbuis.nl
> ---------------------------------
> 
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