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10.1111/biom.13493 http://scihub22266oqcxt.onion/10.1111/biom.13493 34105763!8239582!34105763     free     free     free       Biometrics 2021 ; 77 (4): 1482-1484 Nephropedia Template TP {{tp|p=34105763|t=2021. Discussion on "Improving precision and power in randomized trials for COVID-19 treatments using covariate adjustment for binary, ordinal, and time-to-event outcomes" |pdf=|usr=}}{{34105763}} gab.com Text Discussion on "Improving precision and power in randomized trials for COVID-19 treatments using covariate adjustment for binary, ordinal, and time-to-event outcomes" JO: Biometrics http://www.kidney.de/pget.php?&tw=1&pmid=34105763 #FOAvip Benkeser et al. present a very informative paper evaluating the efficiency gains of covariate adjustment in settings with binary, ordinal, and time-to-event outcomes. The adjustment method focuses on estimating the marginal treatment effect averaged over the covariate distribution in both arms combined. The authors show that covariate adjustment can achieve power gains that could find answers more quickly. The suggested approach is an important weapon in the armamentarium against epidemics like COVID-19. I recommend evaluating the procedure against more traditional approaches for conditional analyses (e.g., logistic regression) and against blinded methods of building prediction models followed by randomization-based inference. Twit Text FOAVip Discussion on "Improving precision and power in randomized trials for COVID-19 treatments using covariate adjustment for binary, ordinal, and time-to-event outcomes" ????Biometrics http://www.kidney.de/pget.php?&tw=1&pmid=34105763 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=34105763 #FOAvip English Wikipedia <ref>{{Cite pmid|34105763}}</ref> Discussion on "Improving precision and power in randomized trials for COVID-19 treatments using covariate adjustment for binary, ordinal, and time-to-event outcomes" #MMPMID34105763 Proschan MA Biometrics 2021[Dec]; 77 (4): 1482-1484 PMID34105763show ga Benkeser et al. present a very informative paper evaluating the efficiency gains of covariate adjustment in settings with binary, ordinal, and time-to-event outcomes. The adjustment method focuses on estimating the marginal treatment effect averaged over the covariate distribution in both arms combined. The authors show that covariate adjustment can achieve power gains that could find answers more quickly. The suggested approach is an important weapon in the armamentarium against epidemics like COVID-19. I recommend evaluating the procedure against more traditional approaches for conditional analyses (e.g., logistic regression) and against blinded methods of building prediction models followed by randomization-based inference. |*COVID-19 Drug Treatment[MESH] |Computer Simulation[MESH] |Humans[MESH] |Randomized Controlled Trials as Topic[MESH]Discussion on "Improving precision and power in randomized trials for (epmc).pdf DeepDyve Pubget Overpricing