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10.1093/biomet/ast045 http://scihub22266oqcxt.onion/10.1093/biomet/ast045 C5490497!5490497!28669998     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 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 243.2 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       Biometrika 2014 ; 101 (1): 237-44 Nephropedia Template TP {{tp|p=28669998|t=2014. On adjustment for auxiliary covariates in additive hazard models for the analysis of randomized experiments |pdf=|usr=}}{{28669998}} gab.com Text On adjustment for auxiliary covariates in additive hazard models for the analysis of randomized experiments JO: Biometrika http://www.kidney.de/pget.php?&tw=1&pmid=28669998 #FOAvip We consider additive hazard models (Aalen, 1989) for the effect of a randomized treatment on a survival outcome, adjusting for auxiliary baseline covariates. We demonstrate that the Aalen least squares estimator of the treatment effect parameter is asymptotically unbiased, even when the hazard?s dependence on time or on the auxiliary covariates is misspecified, and even away from the null hypothesis of no treatment effect. We moreover show that adjustment for auxiliary baseline covariates does not change the asymptotic variance of the Aalen least squares estimator of the effect of a randomized treatment. We conclude that, in view of its robustness against model misspecification, Aalen least squares estimation is attractive for evaluating treatment effects on a survival outcome in randomized experiments, and that the primary reasons to consider baseline covariate adjustment in such settings may be the interest in subgroup effects, or the need to adjust for informative censoring or for baseline imbalances. Our results also shed light on the robustness of Aalen least squares estimators against model misspecification in observational studies. Twit Text FOAVip On adjustment for auxiliary covariates in additive hazard models for the analysis of randomized experiments ????Biometrika http://www.kidney.de/pget.php?&tw=1&pmid=28669998 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=28669998 #FOAvip English Wikipedia <ref>{{Cite pmid|28669998}}</ref> On adjustment for auxiliary covariates in additive hazard models for the analysis of randomized experiments #MMPMID28669998 VANSTEELANDT S; MARTINUSSEN T; TCHETGEN ET Biometrika 2014[Mar]; 101 (1): 237-44 PMID28669998show ga We consider additive hazard models (Aalen, 1989) for the effect of a randomized treatment on a survival outcome, adjusting for auxiliary baseline covariates. We demonstrate that the Aalen least squares estimator of the treatment effect parameter is asymptotically unbiased, even when the hazard?s dependence on time or on the auxiliary covariates is misspecified, and even away from the null hypothesis of no treatment effect. We moreover show that adjustment for auxiliary baseline covariates does not change the asymptotic variance of the Aalen least squares estimator of the effect of a randomized treatment. We conclude that, in view of its robustness against model misspecification, Aalen least squares estimation is attractive for evaluating treatment effects on a survival outcome in randomized experiments, and that the primary reasons to consider baseline covariate adjustment in such settings may be the interest in subgroup effects, or the need to adjust for informative censoring or for baseline imbalances. Our results also shed light on the robustness of Aalen least squares estimators against model misspecification in observational studies. äOn adjustment for auxiliary covariates in additive hazard models for t(epmc).pdf DeepDyve Pubget Overpricing