Yao Jie Xie, Xiaoli Liao, Meijuan Lin, Lin Yang, Kin Cheung, Qingpeng Zhang, Yan Li, Chun Hao, Harry Hx Wang, Yang Gao, Dexing Zhang, Alex Molassiotis, Gilman Kit Hang Siu, Angela Yee Man Leung
Community Engagement in Vaccination Promotion: Systematic Review and Meta-Analysis
JMIR public health and surveillance
bayesian statistics plays pivotal role advancing medical science enabling healthcare companies regulators stakeholders assess safety efficacy new treatments interventions medical procedures bayesian framework offers unique advantage classical framework especially incorporating prior information new trial quality external data historical data another source codata recent years significant increase regulatory submissions using bayesian statistics due flexibility ability provide valuable insights decisionmaking addressing modern complexity clinical trials frequentist trials inadequate regulatory submissions companies often need consider frequentist operating characteristics bayesian analysis strategy regardless design complexity particular focus frequentist type error rate power realistic alternatives tutorial review aims provide comprehensive overview use bayesian statistics sample size determination control type error rate multiplicity adjustments external data borrowing etc regulatory environment clinical trials fundamental concepts bayesian sample size determination illustrative examples provided serve valuable resource researchers clinicians statisticians seeking develop complex innovative designs