Notice: On March 31, it was **announced** that Statalist is moving from an email list to a **forum**. The old list will shut down at the end of May, and its replacement, **statalist.org** is already up and running.

[Date Prev][Date Next][Thread Prev][Thread Next][Date Index][Thread Index]

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 > --------------------------------- > > * > * 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/ * * 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/

**References**:**st: Frailty gamma or inv gaussian distribution***From:*Alexander James <alexandre_lille-paris@hotmail.com>

**Re: st: Frailty gamma or inv gaussian distribution***From:*Maarten Buis <maartenlbuis@gmail.com>

- Prev by Date:
**st: Average marginal effects in ordered probit models** - Next by Date:
**Re: st: Average marginal effects in ordered probit models** - Previous by thread:
**Re: st: Frailty gamma or inv gaussian distribution** - Next by thread:
**st: newey v.s. ivregress 2sls, vce(hac nw)** - Index(es):