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10.1002/sim.7381 http://scihub22266oqcxt.onion/10.1002/sim.7381 C5583028!5583028 !28664662     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 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 Deprecated: Implicit conversion from float 251.2 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 251.2 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\28664662 .jpg): Failed to open stream: No such file or directory in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 117       Stat+Med 2017 ; 36 (22 ): 3560-3572 Nephropedia Template TP {{tp|p=28664662 |t=2017. Functional joint model for longitudinal and time-to-event data: an application to Alzheimer s disease |pdf=|usr=}}{{28664662 }} gab.com Text Functional joint model for longitudinal and time-to-event data: an application to Alzheimer s disease JO: Stat Med http://www.kidney.de/pget.php?&tw=1&pmid=28664662 #FOAvip Functional data are increasingly collected in public health and medical studies to better understand many complex diseases. Besides the functional data, other clinical measures are often collected repeatedly. Investigating the association between these longitudinal data and time to a survival event is of great interest to these studies. In this article, we develop a functional joint model (FJM) to account for functional predictors in both longitudinal and survival submodels in the joint modeling framework. The parameters of FJM are estimated in a maximum likelihood framework via expectation maximization algorithm. The proposed FJM provides a flexible framework to incorporate many features both in joint modeling of longitudinal and survival data and in functional data analysis. The FJM is evaluated by a simulation study and is applied to the Alzheimer's Disease Neuroimaging Initiative study, a motivating clinical study testing whether serial brain imaging, clinical, and neuropsychological assessments can be combined to measure the progression of Alzheimer's disease. Copyright © 2017 John Wiley & Sons, Ltd. Twit Text FOAVip Functional joint model for longitudinal and time-to-event data: an application to Alzheimer s disease ????Stat Med http://www.kidney.de/pget.php?&tw=1&pmid=28664662 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=28664662 #FOAvip English Wikipedia <ref>{{Cite pmid|28664662 }}</ref> Functional joint model for longitudinal and time-to-event data: an application to Alzheimer s disease #MMPMID28664662 Li K ; Luo S Stat Med 2017[Sep]; 36 (22 ): 3560-3572 PMID28664662 show ga Functional data are increasingly collected in public health and medical studies to better understand many complex diseases. Besides the functional data, other clinical measures are often collected repeatedly. Investigating the association between these longitudinal data and time to a survival event is of great interest to these studies. In this article, we develop a functional joint model (FJM) to account for functional predictors in both longitudinal and survival submodels in the joint modeling framework. The parameters of FJM are estimated in a maximum likelihood framework via expectation maximization algorithm. The proposed FJM provides a flexible framework to incorporate many features both in joint modeling of longitudinal and survival data and in functional data analysis. The FJM is evaluated by a simulation study and is applied to the Alzheimer's Disease Neuroimaging Initiative study, a motivating clinical study testing whether serial brain imaging, clinical, and neuropsychological assessments can be combined to measure the progression of Alzheimer's disease. Copyright © 2017 John Wiley & Sons, Ltd. |*Algorithms [MESH] |*Likelihood Functions [MESH] |*Longitudinal Studies [MESH] |*Survival Analysis [MESH] |Alzheimer Disease/physiopathology [MESH] |Computer Simulation [MESH] |Disease Progression [MESH] |Humans [MESH] |Models, Statistical [MESH] |Neuroimaging [MESH]Functional joint model for longitudinal and time-to-event data: an app(epmc).pdf DeepDyve Pubget Overpricing