Effective sample size estimation for a mechanical ventilation trial through Monte-Carlo simulation: Length of mechanical ventilation and Ventilator Free Days

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• 作者：S.E. Morton ; ^a ; ^{sophie.morton@pg.canterbury.ac.nz} ; ^{sophiemorton92@gmail.com} ; Y.S. Chiew^a ; ^d ; ^{chiew.yeong.shiong@monash.edu} ; C. Pretty^a ; ^{chris.pretty@canterbury.ac.nz} ; E. Moltchanova^b ; ^{elena.moltchanova@canterbury.ac.nz} ; C. Scarrott^b ; ^{carl.scarrott@canterbury.ac.nz} ; D. Redmond^a ; ^{daniel.redmond@pg.canterbury.ac.nz} ; G.M. Shaw^c ; ^{geoff.shaw@cdhb.health.nz} ; J.G. Chase^a ; ^{geoff.chase@canterbury.ac.nz}
• 关键词：Statistical significance ; Mechanical ventilation ; Monte-Carlo methods ; Randomised control trials ; Outcome metrics ; Ventilator Free Days
• 刊名：Mathematical Biosciences
• 出版年：2017
• 出版时间：February 2017
• 年：2017
• 卷：284
• 期：Complete
• 页码：21-31
• 全文大小：2567 K
• 卷排序：284

文摘

Many mechanical ventilation RCTs do not achieve significance. LoMV data is highly skewed and normal trial design methods are often ineffective. Monte-Carlo simulation provides a flexible trial design tool. Exclusion criteria improve trial design and better simulate the real trial. Ventilator Free Days can be used as an alternative outcome metric to LoMV.