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10.1007/s43441-021-00297-6 http://scihub22266oqcxt.onion/10.1007/s43441-021-00297-6 33983621!8117454!33983621     free     free     free Deprecated: Implicit conversion from float 219.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 219.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 219.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 219.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       Ther+Innov+Regul+Sci 2021 ; 55 (5): 984-988 Nephropedia Template TP {{tp|p=33983621|t=2021. Implementation of ICH E9 (R1): A Few Points Learned During the COVID-19 Pandemic |pdf=|usr=}}{{33983621}} gab.com Text Implementation of ICH E9 (R1): A Few Points Learned During the COVID-19 Pandemic JO: Ther Innov Regul Sci http://www.kidney.de/pget.php?&tw=1&pmid=33983621 #FOAvip The current COVID-19 pandemic poses numerous challenges for ongoing clinical trials and provides a stress-testing environment for the existing principles and practice of estimands in clinical trials. The pandemic may increase the rate of intercurrent events (ICEs) and missing values, spurring a great deal of discussion on amending protocols and statistical analysis plans to address these issues. In this article, we revisit recent research on estimands and handling of missing values, especially the ICH E9 (R1) Addendum on Estimands and Sensitivity Analysis in Clinical Trials. Based on an in-depth discussion of the strategies for handling ICEs using a causal inference framework, we suggest some improvements in applying the estimand and estimation framework in ICH E9 (R1). Specifically, we discuss a mix of strategies allowing us to handle ICEs differentially based on reasons for ICEs. We also suggest ICEs should be handled primarily by hypothetical strategies and provide examples of different hypothetical strategies for different types of ICEs as well as a road map for estimation and sensitivity analyses. We conclude that the proposed framework helps streamline translating clinical objectives into targets of statistical inference and automatically resolves many issues with defining estimands and choosing estimation procedures arising from events such as the pandemic. Twit Text FOAVip Implementation of ICH E9 (R1): A Few Points Learned During the COVID-19 Pandemic ????Ther Innov Regul Sci http://www.kidney.de/pget.php?&tw=1&pmid=33983621 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=33983621 #FOAvip English Wikipedia <ref>{{Cite pmid|33983621}}</ref> Implementation of ICH E9 (R1): A Few Points Learned During the COVID-19 Pandemic #MMPMID33983621 Qu Y; Lipkovich I Ther Innov Regul Sci 2021[Sep]; 55 (5): 984-988 PMID33983621show ga The current COVID-19 pandemic poses numerous challenges for ongoing clinical trials and provides a stress-testing environment for the existing principles and practice of estimands in clinical trials. The pandemic may increase the rate of intercurrent events (ICEs) and missing values, spurring a great deal of discussion on amending protocols and statistical analysis plans to address these issues. In this article, we revisit recent research on estimands and handling of missing values, especially the ICH E9 (R1) Addendum on Estimands and Sensitivity Analysis in Clinical Trials. Based on an in-depth discussion of the strategies for handling ICEs using a causal inference framework, we suggest some improvements in applying the estimand and estimation framework in ICH E9 (R1). Specifically, we discuss a mix of strategies allowing us to handle ICEs differentially based on reasons for ICEs. We also suggest ICEs should be handled primarily by hypothetical strategies and provide examples of different hypothetical strategies for different types of ICEs as well as a road map for estimation and sensitivity analyses. We conclude that the proposed framework helps streamline translating clinical objectives into targets of statistical inference and automatically resolves many issues with defining estimands and choosing estimation procedures arising from events such as the pandemic. |*COVID-19[MESH] |*Pandemics[MESH] |Data Interpretation, Statistical[MESH] |Humans[MESH] |Research Design[MESH]Implementation of ICH E9 (R1): A Few Points Learned During the COVID-1(epmc).pdf DeepDyve Pubget Overpricing