文章基本信息

标题：Reducing the Length of Mechanical Ventilation with Significance: A Case Study of Sample Size Estimation Trial Design Using Monte-Carlo Simulation
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作者：Yeong Shiong Chiew ; Christopher Pretty ; Elena Moltchanova 等
期刊名称：IFAC PapersOnLine
印刷版ISSN：2405-8963
出版年度：2015
卷号：48
期号：20
页码：273-278
DOI：10.1016/j.ifacol.2015.10.151
语种：English
出版社：Elsevier
摘要：AbstractThe power of a study can be established with estimation of total effective sample size (Ntotai). In this study, the impact of the length of mechanical ventilation (LoMV) distribution shape in intensive care on the estimated Ntotal is investigated. This study provides an overview on the study design involving LoMV, the resulting potential limitations, and the criteria for a 'successful' design. Data from mechanical ventilated patients in a single-center intensive care unit were used in this study. The Ntotal was estimated using two methods: 1) Model-based Altman's nomogram (a standard); and 2) Monte-Carlo simulation. Using Monte-Carlo simulation, a patient selection criteria is imposed to estimate Ntotal from 'realistic' patient cohorts. The Altman nomogram shows that the Ntotal to detect a 25% change in LoMV (ALoMV) at power of 0.8 is >1000 patients. For the Monte-Carlo simulation, a Ntotal >260 patients is needed to detect similar changes. It is important to consider the LoMV distribution shape and variability, particularly relative to target patient groups who might benefit from the intervention. Assessment of ALoMV in response to treatment should be carefully considered to avoid an under-powered studies. The Monte-Carlo simulation combined with objective patient selection provides better design of such studies.
关键词：KeywordsLength of Mechanical VentilationOutcomePower AnalysisSample Size