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10.1002/sim.6967 http://scihub22266oqcxt.onion/10.1002/sim.6967 C4965318!4965318!27090375     free     free     free Deprecated: Implicit conversion from float 209.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 209.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 209.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 209.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 209.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       Stat+Med 2016 ; 35 (21): 3733-44 Nephropedia Template TP {{tp|p=27090375|t=2016. Improving Power in Small-Sample Longitudinal Studies when using Generalized Estimating Equations |pdf=|usr=}}{{27090375}} gab.com Text Improving Power in Small-Sample Longitudinal Studies when using Generalized Estimating Equations JO: Stat Med http://www.kidney.de/pget.php?&tw=1&pmid=27090375 #FOAvip Generalized estimating equations (GEE) are often used for the marginal analysis of longitudinal data. Although much work has been done to improve the validity of GEE for the analysis of data arising from small-sample studies, little attention has been given to power in such settings. Therefore, we propose a valid GEE approach to improve power in small-sample longitudinal study settings in which the temporal spacing of outcomes is the same for each subject. Specifically, we use a modified empirical sandwich covariance matrix estimator within correlation structure selection criteria and test statistics. Use of this estimator can improve the accuracy of selection criteria and increase the degrees of freedom to be used for inference. The resulting impacts on power are demonstrated via a simulation study and application example. Twit Text FOAVip Improving Power in Small-Sample Longitudinal Studies when using Generalized Estimating Equations ????Stat Med http://www.kidney.de/pget.php?&tw=1&pmid=27090375 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=27090375 #FOAvip English Wikipedia <ref>{{Cite pmid|27090375}}</ref> Improving Power in Small-Sample Longitudinal Studies when using Generalized Estimating Equations #MMPMID27090375 Westgate PM; Burchett WW Stat Med 2016[Sep]; 35 (21): 3733-44 PMID27090375show ga Generalized estimating equations (GEE) are often used for the marginal analysis of longitudinal data. Although much work has been done to improve the validity of GEE for the analysis of data arising from small-sample studies, little attention has been given to power in such settings. Therefore, we propose a valid GEE approach to improve power in small-sample longitudinal study settings in which the temporal spacing of outcomes is the same for each subject. Specifically, we use a modified empirical sandwich covariance matrix estimator within correlation structure selection criteria and test statistics. Use of this estimator can improve the accuracy of selection criteria and increase the degrees of freedom to be used for inference. The resulting impacts on power are demonstrated via a simulation study and application example. äImproving Power in Small-Sample Longitudinal Studies when using Genera(epmc).pdf DeepDyve Pubget Overpricing