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10.1111/biom.12298 http://scihub22266oqcxt.onion/10.1111/biom.12298 C4567543!4567543!25762198     free     free     free Deprecated: Implicit conversion from float 217.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 217.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 217.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 217.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       Biometrics 2015 ; 71 (3): 760-71 Nephropedia Template TP {{tp|p=25762198|t=2015. Bayesian Path Specific Frailty Models for Multi-state Survival Data with Applications |pdf=|usr=}}{{25762198}} gab.com Text Bayesian Path Specific Frailty Models for Multi-state Survival Data with Applications JO: Biometrics http://www.kidney.de/pget.php?&tw=1&pmid=25762198 #FOAvip Multi-state models can be viewed as generalizations of both the standard and competing risks models for survival data. Models for multi-state data have been the theme of many recent published works. Motivated by bone marrow transplant data, we propose a Bayesian model using the gap times between two successive events in a path of events experienced by a subject. Path specific frailties are introduced to capture the dependence structure of the gap times in the paths with two or more states. Under improper prior distributions for the parameters, we establish propriety of the posterior distribution. An efficient Gibbs sampling algorithm is developed for drawing samples from the posterior distribution. An extensive simulation study is carried out to examine the empirical performance of the proposed approach. A bone marrow transplant data set is analyzed in detail to further demonstrate the proposed methodology. Twit Text FOAVip Bayesian Path Specific Frailty Models for Multi-state Survival Data with Applications ????Biometrics http://www.kidney.de/pget.php?&tw=1&pmid=25762198 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=25762198 #FOAvip English Wikipedia <ref>{{Cite pmid|25762198}}</ref> Bayesian Path Specific Frailty Models for Multi-state Survival Data with Applications #MMPMID25762198 de Castro M; Chen MH; Zhang Y Biometrics 2015[Sep]; 71 (3): 760-71 PMID25762198show ga Multi-state models can be viewed as generalizations of both the standard and competing risks models for survival data. Models for multi-state data have been the theme of many recent published works. Motivated by bone marrow transplant data, we propose a Bayesian model using the gap times between two successive events in a path of events experienced by a subject. Path specific frailties are introduced to capture the dependence structure of the gap times in the paths with two or more states. Under improper prior distributions for the parameters, we establish propriety of the posterior distribution. An efficient Gibbs sampling algorithm is developed for drawing samples from the posterior distribution. An extensive simulation study is carried out to examine the empirical performance of the proposed approach. A bone marrow transplant data set is analyzed in detail to further demonstrate the proposed methodology. äBayesian Path Specific Frailty Models for Multi-state Survival Data wi(epmc).pdf DeepDyve Pubget Overpricing