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10.1111/biom.12163 http://scihub22266oqcxt.onion/10.1111/biom.12163 C4153804!4153804!24588756     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 Warning: imagejpeg(C:\Inetpub\vhosts\kidney.de\httpdocs\phplern\24588756.jpg): Failed to open stream: No such file or directory in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 117       Biometrics 2014 ; 70 (3): 579-87 Nephropedia Template TP {{tp|p=24588756|t=2014. Accelerated Intensity Frailty Model for Recurrent Events Data |pdf=|usr=}}{{24588756}} gab.com Text Accelerated Intensity Frailty Model for Recurrent Events Data JO: Biometrics http://www.kidney.de/pget.php?&tw=1&pmid=24588756 #FOAvip In this article we propose an accelerated intensity frailty (AIF) model for recurrent events data and derive a test for the variance of frailty. In addition, we develop a kernel-smoothing-based EM algorithm for estimating regression coefficients and the baseline intensity function. The variance of the resulting estimator for regression parameters is obtained by a numerical differentiation method. Simulation studies are conducted to evaluate the finite sample performance of the proposed estimator under practical settings and demonstrate the efficiency gain over the Gehan rank estimator based on the AFT model for counting process (Lin et al., 1998, Biometrika 85, 605?618). Our method is further illustrated with an application to a bladder tumor recurrence data. Twit Text FOAVip Accelerated Intensity Frailty Model for Recurrent Events Data ????Biometrics http://www.kidney.de/pget.php?&tw=1&pmid=24588756 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=24588756 #FOAvip English Wikipedia <ref>{{Cite pmid|24588756}}</ref> Accelerated Intensity Frailty Model for Recurrent Events Data #MMPMID24588756 Liu B; Lu W; Zhang J Biometrics 2014[Sep]; 70 (3): 579-87 PMID24588756show ga In this article we propose an accelerated intensity frailty (AIF) model for recurrent events data and derive a test for the variance of frailty. In addition, we develop a kernel-smoothing-based EM algorithm for estimating regression coefficients and the baseline intensity function. The variance of the resulting estimator for regression parameters is obtained by a numerical differentiation method. Simulation studies are conducted to evaluate the finite sample performance of the proposed estimator under practical settings and demonstrate the efficiency gain over the Gehan rank estimator based on the AFT model for counting process (Lin et al., 1998, Biometrika 85, 605?618). Our method is further illustrated with an application to a bladder tumor recurrence data. äAccelerated Intensity Frailty Model for Recurrent Events Data (epmc).pdf DeepDyve Pubget Overpricing