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10.1080/10543406.2021.1897992

http://scihub22266oqcxt.onion/10.1080/10543406.2021.1897992
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34406917!ä!34406917

suck abstract from ncbi


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pmid34406917      J+Biopharm+Stat 2023 ; 33 (4): 403-424
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  • Estimands, Handling of Missing Data and Impact on Assumed Effect Size and Power in Pivotal COVID-19 Treatment Trials #MMPMID34406917
  • Chakladar S; Liao R; Gamalo M
  • J Biopharm Stat 2023[Jul]; 33 (4): 403-424 PMID34406917show ga
  • Estimands play an important role for aligning study objectives, study design and analyses through a precise definition of the quantity of interest. For COVID-19 studies, apart from intercurrent events, high volume of missing data has been observed. We explore their impact on several estimands through a synthetic COVID-19 data generated from a discrete-time multi-state model. We compare estimators of these estimands based on their ability to closely match the true response rates and retain assumed power. The final choice of the estimand then needs to be aligned with clinically meaningful quantities of interest to patients, clinicians, regulators and payers.
  • |*COVID-19[MESH]
  • |COVID-19 Drug Treatment[MESH]
  • |Humans[MESH]
  • |Models, Statistical[MESH]


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