Use my Search Websuite to scan PubMed, PMCentral, Journal Hosts and Journal Archives, FullText. Kick-your-searchterm to multiple Engines kick-your-query now !>

A dictionary by aggregated review articles of nephrology, medicine and the life sciences Your one-stop-run pathway from word to the immediate pdf of peer-reviewed on-topic knowledge.

10.1177/1471082X14565526 http://scihub22266oqcxt.onion/10.1177/1471082X14565526 C4591554!4591554!26441487     free     free     free Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       Stat+Modelling 2015 ; 15 (3): 256-78 Nephropedia Template TP {{tp|p=26441487|t=2015. Cox Regression Models with Functional Covariates for Survival Data |pdf=|usr=}}{{26441487}} gab.com Text Cox Regression Models with Functional Covariates for Survival Data JO: Stat Modelling http://www.kidney.de/pget.php?&tw=1&pmid=26441487 #FOAvip We extend the Cox proportional hazards model to cases when the exposure is a densely sampled functional process, measured at baseline. The fundamental idea is to combine penalized signal regression with methods developed for mixed effects proportional hazards models. The model is fit by maximizing the penalized partial likelihood, with smoothing parameters estimated by a likelihood-based criterion such as AIC or EPIC. The model may be extended to allow for multiple functional predictors, time varying coefficients, and missing or unequally-spaced data. Methods were inspired by and applied to a study of the association between time to death after hospital discharge and daily measures of disease severity collected in the intensive care unit, among survivors of acute respiratory distress syndrome. Twit Text FOAVip Cox Regression Models with Functional Covariates for Survival Data ????Stat Modelling http://www.kidney.de/pget.php?&tw=1&pmid=26441487 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=26441487 #FOAvip English Wikipedia <ref>{{Cite pmid|26441487}}</ref> Cox Regression Models with Functional Covariates for Survival Data #MMPMID26441487 Gellar JE; Colantuoni E; Needham DM; Crainiceanu CM Stat Modelling 2015[Jun]; 15 (3): 256-78 PMID26441487show ga We extend the Cox proportional hazards model to cases when the exposure is a densely sampled functional process, measured at baseline. The fundamental idea is to combine penalized signal regression with methods developed for mixed effects proportional hazards models. The model is fit by maximizing the penalized partial likelihood, with smoothing parameters estimated by a likelihood-based criterion such as AIC or EPIC. The model may be extended to allow for multiple functional predictors, time varying coefficients, and missing or unequally-spaced data. Methods were inspired by and applied to a study of the association between time to death after hospital discharge and daily measures of disease severity collected in the intensive care unit, among survivors of acute respiratory distress syndrome. äCox Regression Models with Functional Covariates for Survival Data (epmc).pdf DeepDyve Pubget Overpricing