相加风险模型下的样本量。
Sample size under the additive hazards model.
作者信息
McDaniel Lee S, Yu Menggang, Chappell Rick
机构信息
Biostatistics Program, School of Public Health, Louisiana State University Health Sciences Center, New Orleans, LA, USA
Department of Biostatistics & Medical Informatics, University of Wisconsin-Madison, Madison, WI, USA.
出版信息
Clin Trials. 2016 Apr;13(2):188-98. doi: 10.1177/1740774515614542. Epub 2015 Nov 15.
BACKGROUND
The additive hazards model can be easier to interpret and in some cases fits better than the proportional hazards model. However, sample size formulas for clinical trials with time to event outcomes are currently based on either the proportional hazards assumption or an assumption of constant hazards.
AIMS
The goal is to provide sample size formulas for superiority and non-inferiority trials assuming an additive hazards model but no specific distribution, along with evaluations of the performance of the formulas.
METHODS
Formulas are presented that determine the required sample size for a given scenario under the additive hazards model. Simulations are conducted to ensure that the formulas attain the desired power. For illustration, the non-inferiority sample size formula is applied to the calculations in the SPORTIF III trial of stroke prevention in atrial fibrillation.
CONCLUSION
Simulation results show that the sample size calculations lead to the correct power. Sample size is easily calculated using a tool that is available on the web at http://leemcdaniel.github.io/samplesize.html.
背景
相加风险模型可能更易于解释，并且在某些情况下比比例风险模型拟合得更好。然而，具有事件发生时间结局的临床试验的样本量公式目前基于比例风险假设或恒定风险假设。
目的
目标是提供基于相加风险模型但无特定分布的优效性和非劣效性试验的样本量公式，以及对这些公式性能的评估。
方法
给出了在相加风险模型下确定给定场景所需样本量的公式。进行模拟以确保公式达到所需的检验效能。为作说明，将非劣效性样本量公式应用于房颤卒中预防的SPORTIF III试验的计算中。
结论
模拟结果表明样本量计算得出了正确的检验效能。可使用网址为http://leemcdaniel.github.io/samplesize.html的网络工具轻松计算样本量。
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