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From |
"Stephen P. Jenkins" <stephenj@essex.ac.uk> |

To |
<statalist@hsphsun2.harvard.edu> |

Subject |
st: Survival analysis: competing risks models with fraility [Was: Fwd: Request for your papers] |

Date |
Mon, 17 May 2010 09:19:25 +0100 |

========================================== Date: Sun, 16 May 2010 19:30:23 -0400 From: Sridhar Telidevara <sridhar.telidevara@gmail.com> Subject: st: Fwd: Request for your papers I ran two log-logistic competing risks models, one with and another without gamma heterogeneity (mean 1 and variance 1/theta). There is a huge difference between the two likelihoods (likelihood-ratio test holds), but the estimate for the variance term (1/theta) is pretty close to zero and also insignificant. I know that if the estimate of the variance parameter for gamma heterogeneity is zero then the model should converge to the model without heterogeneity (independent risks) and the likelihood ratios of the two models should pretty much be the same. I am not sure how to interpret the results. I highly appreciate your input in this regard. Thank you, Regards, Sridhar Telidevara =========================================== (1) please use properly informative "Subject:" headers in your posts (2) You'll need to provide more details about the models you think you've estimated and/or give a precise reference to an article or book which describes the model. It sounds like you have used -streg <...>, dist(weibull) frailty(gamma)- twice, once for each risk. If this is the case, then the "competing risks" model that you estimated is rather weird. Put another way, you are assuming unobserved gamma variability in each of the two marginal distributions for the latent hazards, but are assuming zero correlation between them -- which is unrealistic. The 'proper' way to incorporate unobserved heterogeneity in a competing risk model is to use a multivariate distribution for the heterogeneity, allowing for correlation. If you assume a continuous frailty distribution, then assuming multivariate normality is the easiest way to go. Alternatively, assume a multivariate discrete distribution (a series of mass points with associated probabilities). Either way, you'll likely have to program your own likelihood function using -ml-. Stephen ------------------------------------- Professor Stephen P. Jenkins <stephenj@essex.ac.uk> Institute for Social and Economic Research (ISER) University of Essex, Colchester CO4 3SQ, UK Tel: +44(0)1206 873374. Fax: +44(0)1206 873151 http://www.iser.essex.ac.uk Survival Analysis using Stata: http://www.iser.essex.ac.uk/survival-analysis Downloadable papers and software: http://ideas.repec.org/e/pje7.html * * 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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