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10.1111/biom.12748 http://scihub22266oqcxt.onion/10.1111/biom.12748 C5832538!5832538 !28863246     free     free     free Warning: imagejpeg(C:\Inetpub\vhosts\kidney.de\httpdocs\phplern\28863246 .jpg): Failed to open stream: No such file or directory in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 117       Biometrics 2018 ; 74 (1 ): 109-117 Nephropedia Template TP {{tp|p=28863246 |t=2018. FLCRM: Functional linear cox regression model |pdf=|usr=}}{{28863246 }} gab.com Text FLCRM: Functional linear cox regression model JO: Biometrics http://www.kidney.de/pget.php?&tw=1&pmid=28863246 #FOAvip We consider a functional linear Cox regression model for characterizing the association between time-to-event data and a set of functional and scalar predictors. The functional linear Cox regression model incorporates a functional principal component analysis for modeling the functional predictors and a high-dimensional Cox regression model to characterize the joint effects of both functional and scalar predictors on the time-to-event data. We develop an algorithm to calculate the maximum approximate partial likelihood estimates of unknown finite and infinite dimensional parameters. We also systematically investigate the rate of convergence of the maximum approximate partial likelihood estimates and a score test statistic for testing the nullity of the slope function associated with the functional predictors. We demonstrate our estimation and testing procedures by using simulations and the analysis of the Alzheimer's Disease Neuroimaging Initiative (ADNI) data. Our real data analyses show that high-dimensional hippocampus surface data may be an important marker for predicting time to conversion to Alzheimer's disease. Data used in the preparation of this article were obtained from the ADNI database (adni.loni.usc.edu). Twit Text FOAVip FLCRM: Functional linear cox regression model ????Biometrics http://www.kidney.de/pget.php?&tw=1&pmid=28863246 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=28863246 #FOAvip English Wikipedia <ref>{{Cite pmid|28863246 }}</ref> FLCRM: Functional linear cox regression model #MMPMID28863246 Kong D ; Ibrahim JG ; Lee E ; Zhu H Biometrics 2018[Mar]; 74 (1 ): 109-117 PMID28863246 show ga We consider a functional linear Cox regression model for characterizing the association between time-to-event data and a set of functional and scalar predictors. The functional linear Cox regression model incorporates a functional principal component analysis for modeling the functional predictors and a high-dimensional Cox regression model to characterize the joint effects of both functional and scalar predictors on the time-to-event data. We develop an algorithm to calculate the maximum approximate partial likelihood estimates of unknown finite and infinite dimensional parameters. We also systematically investigate the rate of convergence of the maximum approximate partial likelihood estimates and a score test statistic for testing the nullity of the slope function associated with the functional predictors. We demonstrate our estimation and testing procedures by using simulations and the analysis of the Alzheimer's Disease Neuroimaging Initiative (ADNI) data. Our real data analyses show that high-dimensional hippocampus surface data may be an important marker for predicting time to conversion to Alzheimer's disease. Data used in the preparation of this article were obtained from the ADNI database (adni.loni.usc.edu). |*Linear Models [MESH] |*Proportional Hazards Models [MESH] |Alzheimer Disease/diagnostic imaging/pathology [MESH] |Hippocampus/diagnostic imaging/pathology [MESH] |Humans [MESH] |Likelihood Functions [MESH] |Models, Statistical [MESH] |Neuroimaging [MESH] |Principal Component Analysis [MESH]FLCRM: Functional linear cox regression model (epmc).pdf DeepDyve Pubget Overpricing