ICHPS2027
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Contributed

Trials & RWE

Thu, Jan 14, 11:00 AM - 12:45 PM

6 Abstracts

A Bayesian Adaptive Hybrid Controlled Trial Design for Integrating Real-World Evidence in Rare Diseases
Peijin Wang (Duke University School of Medicine Dept. of Biostatistics & Bioinformation)
Co-authors: Hwanhee Hong (Duke University)
Rare disease drug developmentReal-world EvidenceExternal controlsBayesian adaptive designSample size re-estimation.
Toward Inclusive Real-World Evidence: A Representativeness Framework for PCORnet
Carly Brantner (Duke University)
Co-authors: Laine Thomas (Duke University)
representativenessgeneralizabilityreal-world datadata networksPCORnetstudy designhealth equitycausal inference
An E-value-Informed Sensitivity Analysis Framework for Hybrid Controlled Trials
Chunnan Liu
Co-authors: Lizbeth F. Gomez (Rutgers University), Rebecca Hubbard (Brown University), Kimberly Lactaoen (University of Pennsylvania), Melanie Mayer (University of Pennsylvania), Gary E. Weissman (University of Pennsylvania)
Hybrid controlled trialssensitivity analysisE-valueunmeasured confoundingreal-world dataclinical trial design
Emulating Stepped-Wedge Cluster Randomized Trials to Evaluate Health Policies and Interventions
Haidong Lu (Yale University)
Co-authors: Gregg Gonsalves (Yale University), Lee Kennedy-Shaffer (Yale University), Fan Li (Yale University), Guangyu Tong (Yale University)
Causal InferenceHealth PolicyObservational StudiesPragmatic Clinical TrialsQuasi-ExperimentsTarget Trial Emulation
Nesting a Target Study within a Target Trial: A Framework for Evaluating Intervention Effects on Disparities
Xinyi Sun (Johns Hopkins Bloomberg School of Public Health)
Co-authors: Deidra C Crews (Department of Medicine, Johns Hopkins School of Medicine), Emmanuel Drabo, Kadija Ferryman (Berman Institute of Bioethics, Johns Hopkins University), Theodore J Iwashyna (Department of Medicine, Johns Hopkins School of Medicine), John Jackson
DisparityCausal InferenceEthicsFairnessAllowabilityTarget Study EmulationTarget Trial EmulationInterventions
Addressing confounding by indication through (un)measured center characteristics in Learn-As-you-GO (LAGO) trials
Minh Thu Bui
Co-authors: Ante Bing (Boston University), Hayden B. Bosworth (Duke University), Judith J. Lok (Boston University), Chris T. Longenecker (University of Washington), Donna Spiegelman (Yale University), Allison R. Webel (University of Washington)
unmeasured confoundingdependent sampleadaptive trialimplementation trial
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