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10.1002/sim.4201 http://scihub22266oqcxt.onion/10.1002/sim.4201 C4453987!4453987!21341300     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 Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       Stat+Med 2011 ; 30 (10): 1137-56 Nephropedia Template TP {{tp|p=21341300|t=2011. A functional multiple imputation approach to incomplete longitudinal data |pdf=|usr=}}{{21341300}} gab.com Text A functional multiple imputation approach to incomplete longitudinal data JO: Stat Med http://www.kidney.de/pget.php?&tw=1&pmid=21341300 #FOAvip In designed longitudinal studies, information from the same set of subjects are collected repeatedly over time. The longitudinal measurements are often subject to missing data which impose an analytic challenge. We propose a functional multiple imputation approach modeling longitudinal response profiles as smooth curves of time under a functional mixed effects model. We develop a Gibbs sampling algorithm to draw model parameters and imputations for missing values, using a blocking technique for an increased computational efficiency. In an illustrative example, we apply a multiple imputation analysis to data from the Panel Study of Income Dynamics and the Child Development Supplement to investigate the gradient effect of family income on children's health status. Our simulation study demonstrates that this approach performs well under varying modeling assumptions on the time trajectory functions and missingness patterns. Twit Text FOAVip A functional multiple imputation approach to incomplete longitudinal data ????Stat Med http://www.kidney.de/pget.php?&tw=1&pmid=21341300 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=21341300 #FOAvip English Wikipedia <ref>{{Cite pmid|21341300}}</ref> A functional multiple imputation approach to incomplete longitudinal data #MMPMID21341300 He Y; Yucel R; Raghunathan TE Stat Med 2011[May]; 30 (10): 1137-56 PMID21341300show ga In designed longitudinal studies, information from the same set of subjects are collected repeatedly over time. The longitudinal measurements are often subject to missing data which impose an analytic challenge. We propose a functional multiple imputation approach modeling longitudinal response profiles as smooth curves of time under a functional mixed effects model. We develop a Gibbs sampling algorithm to draw model parameters and imputations for missing values, using a blocking technique for an increased computational efficiency. In an illustrative example, we apply a multiple imputation analysis to data from the Panel Study of Income Dynamics and the Child Development Supplement to investigate the gradient effect of family income on children's health status. Our simulation study demonstrates that this approach performs well under varying modeling assumptions on the time trajectory functions and missingness patterns. äA functional multiple imputation approach to incomplete longitudinal d(epmc).pdf DeepDyve Pubget Overpricing