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UID:20251121T210356EST-2284mtMT13@132.216.98.100
DTSTAMP:20251122T020356Z
DESCRIPTION:Title: Power Calculation for Detecting Interaction Effect in Cr
 oss-Sectional Stepped-Wedge Cluster-Randomized Trials.\n\nAbstract:Deukwoo
  Kwon is an Associate Professor at the Institute for Healthcare Delivery S
 cience in the Department of Population Health Science and Policy and a bio
 statistician at the Biostatistics Shared Resource Facility\, Tisch Cancer 
 Institute (TCI) at Icahn School of Medicine at Mount Sinai. He earned his 
 Master and Ph.D. degrees in statistics from Texas A&M University and worke
 d at the National Cancer Institute (NCI) for six years. At NCI\, Dr. Kwon 
 worked on various epidemiologic studies including radiation exposure asses
 sment\, uncertainty analysis\, and measurement error models in dose-respon
 se relationship. Before joining Icahn School of Medicine at Mount Sinai in
  February 2022\, he worked at University of Miami over 10 years and gained
  extensive experience in developing optimal statistical design and conduct
 ing analysis for cancer clinical trials and observational studies. He has 
 utilized survival analysis\, longitudinal data analysis\, cancer registry 
 data analysis\, Bayesian inference\, and high-dimensional data analysis fo
 r his collaborative work. He is a member of Protocol Review and Monitoring
  Committee at TCI where he promotes use of emerging approaches to design a
 nd analysis of phase I and phase II cancer clinical trials.\n	\n	Madhu Mazum
 dar is Director of the Institute for Healthcare Delivery Science at the Mo
 unt Sinai Health System and is a Professor of Biostatistics at the Center 
 of Biostatistics\, Department of Population Health Science and Policy. She
  also directs the Biostatistics Core of Tisch Cancer Institute. Website: h
 ttps://profiles.mountsinai.org/madhu-mazumdar\n\n\nStepped-Wedge Cluster-R
 andomized Trials (SW-CRTs) are increasingly utilized for evaluating comple
 x healthcare delivery interventions where simple CRTs are not feasible. Ap
 pealing features of SW-CRTs include having each cluster acting as their ow
 n control\, not needing to withhold the intervention from any patient\, an
 d having time to prepare clusters for administration of intervention while
  collecting baseline information. However\, the design and analysis of SW-
 CRT is complex and methodology is not available for many scenarios includi
 ng detection of interaction effects. Detecting interaction effect is impor
 tant for a variety of research scenarios. We present four ways of computin
 g power and showcase their comparative performance through simulation. We 
 then apply the methodology to a published SW-CRT with binary outcome. Exte
 nsion to continuous and censored outcomes are underway.\n\nhttp://www.crm.
 umontreal.ca/cal/en/www.mcgill.ca/epi-biostat-occh/news-e...\n
DTSTART:20220914T193000Z
DTEND:20220914T203000Z
SUMMARY:Deukwoo Kwon\, PhD & Madhu Mazumdar\, PhD\, Mount Sinai
URL:/mathstat/channels/event/deukwoo-kwon-phd-madhu-ma
 zumdar-phd-mount-sinai-341771
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