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10.1111/j.1541-0420.2009.01334.x http://scihub22266oqcxt.onion/10.1111/j.1541-0420.2009.01334.x C4593705!4593705!19817741     free     free     free Deprecated: Implicit conversion from float 213.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 213.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 213.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 213.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 213.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 213.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       Biometrics 2010 ; 66 (3): 824-33 Nephropedia Template TP {{tp|p=19817741|t=2010. Estimating treatment effects of longitudinal designs using regression models on propensity scores |pdf=|usr=}}{{19817741}} gab.com Text Estimating treatment effects of longitudinal designs using regression models on propensity scores JO: Biometrics http://www.kidney.de/pget.php?&tw=1&pmid=19817741 #FOAvip We derive regression estimators that can compare longitudinal treatments using only the longitudinal propensity scores as regressors. These estimators, which assume knowledge of the variables used in the treatment assignment, are important for reducing the large dimension of covariates for two reasons. First, if the regression models on the longitudinal propensity scores are correct, then our estimators share advantages of correctly-specified model-based estimators, a benefit not shared by estimators based on weights alone. Second, if the models are incorrect, the misspecification can be more easily limited through model checking than with models based on the full covariates. Thus, our estimators can also be better when used in place of the regression on the full covariates. We use our methods to compare longitudinal treatments for type 2 diabetes mellitus. Twit Text FOAVip Estimating treatment effects of longitudinal designs using regression models on propensity scores ????Biometrics http://www.kidney.de/pget.php?&tw=1&pmid=19817741 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=19817741 #FOAvip English Wikipedia <ref>{{Cite pmid|19817741}}</ref> Estimating treatment effects of longitudinal designs using regression models on propensity scores #MMPMID19817741 Achy-Brou AC; Frangakis CE; Griswold M Biometrics 2010[Sep]; 66 (3): 824-33 PMID19817741show ga We derive regression estimators that can compare longitudinal treatments using only the longitudinal propensity scores as regressors. These estimators, which assume knowledge of the variables used in the treatment assignment, are important for reducing the large dimension of covariates for two reasons. First, if the regression models on the longitudinal propensity scores are correct, then our estimators share advantages of correctly-specified model-based estimators, a benefit not shared by estimators based on weights alone. Second, if the models are incorrect, the misspecification can be more easily limited through model checking than with models based on the full covariates. Thus, our estimators can also be better when used in place of the regression on the full covariates. We use our methods to compare longitudinal treatments for type 2 diabetes mellitus. äEstimating treatment effects of longitudinal designs using regression (epmc).pdf DeepDyve Pubget Overpricing