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SUMMARY:Grand Rounds April 5\, 2024: A New Look at P Values for Randomized Clinical Trials (Erik van Zwet\, PhD)
DESCRIPTION:Speaker: \nErik van Zwet\, PhD\nDepartment of Biomedical Data Sciences\nLeiden University Medical Center\, the Netherlands \nTitle: A New Look at P Values for Randomized Clinical Trials \nDate: Friday\, April 5\, 2024\, 1:00-2:00 p.m. ET \nTo join the online meeting: \nClick the Zoom Meeting link below: \nhttps://duke.zoom.us/j/98292370425?pwd=S05sOU42WXp3NXNjK0ZkczN0WXpHUT09 \nMeeting ID: 982 9237 0425 \nPasscode: 237056 \nOne Tap Mobile \n+13126266799\,\,98292370425#\,\,\,\,*237056# US (Chicago) \n+16468769923\,\,98292370425#\,\,\,\,*237056# US (New York) \nFind your local number: https://duke.zoom.us/u/acEzZCxM5z \nAudio Dial in Options \nUS:  +1 309 205 3325 US \nInternational: https://duke.zoom.us/u/acGjMcvNR6 \nMeeting ID: 982 9237 0425 \nPasscode: 237056
URL:https://rethinkingclinicaltrials.org/event/grand-rounds-april-5-2024-a-new-look-at-p-values-for-randomized-clinical-trials-erik-van-zwet-phd/
CATEGORIES:Grand Rounds Event
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UID:33649-1712323800-1712327400@rethinkingclinicaltrials.org
SUMMARY:2024 Allan Donner Lecture: The Unit of Inference in Cluster Randomized Trials
DESCRIPTION:2024 Allan Donner Lecture: Dr. Fan Li\nDate: Friday\, April 5\nTime: 1:30 pm – 2:30 pm\nLocation: PHFM 3015 (Western Centre for Public Health and Family Medicine) or Zoom (link may be requested at EpiBio@uwo.ca)\nLink: https://www.schulich.uwo.ca/epibio/about_us/events/2024/2024_Allan_Donner_Lecture.html \nThe unit of inference in cluster randomized trials\nFan Li\n\nAssistant Professor \nDepartment of Biostatistics\nYale School of Public Health\,\nYale Center for Methods in Implementation and Prevention Science \nShort Biography:\nFan Li is a tenure-track Assistant Professor in the Department of Biostatistics at Yale School of Public Health\, and Yale Center for Methods in Implementation and Prevention Science. He obtained his PhD in Biostatistics from Duke University in 2019 and joined the Yale faculty since 2019. His research aims to develop methods for designing and analyzing cluster randomized trials\, causal inference methods for estimand-aligned analyses of randomized experiments and observational studies. Dr. Li’s methodology research has been funded by multiple awards from the United States National Institutes of Health and Patient-Centered Outcome Research Institute as a principal investigator and a co-investigator. He has published over 120 peer-reviewed articles. He is Co-Editor in Chief of the journal\, Epidemiologic Methods\, and Associate Editor of several journals including Statistics in Medicine\, Clinical Trials\, and Implementation Science. \nAbstract: \nIn cluster randomized trials\, intact clusters of individuals rather than individuals themselves are randomly assigned to treatment conditions\, creating a two-level structure that complicates the design and analysis compared to individually randomized trials. While the need to address intracluster correlations has motivated a robust literature for designing and analyzing cluster randomized trials in the past two decades\, fewer efforts have integrated these developments in the context of treatment effect estimands. On page 13 of Donner and Klar (2000)\, it has already been suggested that “the target of inference in such studies (CRTs) could be at either the individual level or community level”\, but the philosophy around unit of inference seems to be lost in translation until recently. In this presentation\, we emphasize the difference between the unit of inference and the unit of analysis\, and use the potential outcomes notation to define estimands that represent the unit of inference\, regardless of the unit of analysis. In addition\, we explain how one can standardize the output from any familiar regression model to ensure estimand-aligned inference. A key take-away is that one does not need to forgo the conventional wisdom developed in the existing literature to obtain the right inferential target\, as long as the unit of inference is specified a priori\, and a robust standardization procedure is applied to process the regression model output. \nKeywords:\nCluster randomized trials; Stepped wedge designs; Causal inference; Estimands; Observational studies; Semiparametric methods. \nWebsite
URL:https://rethinkingclinicaltrials.org/event/2024-allan-donner-lecture-the-unit-of-inference-in-cluster-randomized-trials/
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