```Date: Mon, 2 May 2005 17:33:46 +0200 Reply-To: Stefan Pohl Sender: "SAS(r) Discussion" From: Stefan Pohl Subject: ML estimation for clustered data (shared frailty model) Content-Type: text/plain; charset="iso-8859-1" Hi all! Sorry, I posted this message one week ago, but nobody answered to me. Today I post my message again hoping that anybody is able to give me some good advice. I want to estimate by maximum likelihood a parametric shared gamma frailty model. My data consist of the triple (T_ij, d_ij, Z_ij). T_ij is the event time of subject j in group i (i= 1,...,G and j=1,...,n_i). d_ij is the censoring indicator of subject j in group i and Z_ij is a covariate of subject j in group i. Let D_i = sum_(j=1)^(n_i) d_ij be the number of events in group i. Then the log likelihood is given by llik(theta,beta) = sum_(i=1)^(G) D_i * ln(theta) - ln[Gam(1/theta)] + ln[Gam(1/theta + D_i)] - (1/theta + D_i) * ln[1 + theta sum_(j=1)^(n_i) H_0 (T_ij) * exp(beta Z_ij)] + sum_(j=1)^(n_i) d_ij * [beta Z_ij + ln(h_0 (T_ij) )] Gam is the gamma function, theta is the variance of a gamma random variable, H_0 is a cumulative baseline hazard function (which has to be specified), h_0 is a baseline hazard function (which has to be specified). Which procedure in SAS should I use? The general PROC NLP? How can I take into account that my data are clustered in G groups in this procedure? To estimate a weibull shared normal frailty model I used proc nlmixed. In proc nlmixed the data are clustered according to the subject= variable. But the only available frailty distribution in proc nlmixed is the normal distribution. Thank you for your help, my best regards, Stefan. ```

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