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Modelling the patient accrual with truncated Gaussian mixture distribution for the accurate estimation of sample size in survival trials

机译:用截短的高斯混合分布模拟患者应计,以准确估算存活试验中的样本量

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摘要

In survival trials with fixed trial length, the patient accrual rate has a significant impact on the sample size estimation or equivalently, on the power of trials. A larger sample size is required for the staggered patient entry. During enrollment, the patient accrual rate changes with the recruitment publicity effect, disease incidence and many other factors and fluctuations of the accrual rate occur frequently. However, the existing accrual models are either over-simplified for the constant rate assumption or complicated in calculation for the subdivision of the accrual period. A more flexible accrual model is required to represent the fluctuant patient accrual rate for accurate sample size estimation. In this paper, inspired by the flexibility of the Gaussian mixture distribution in approximating continuous densities, we propose the truncated Gaussian mixture distribution accrual model to represent different variations of accrual rate by different parameter configurations. The sample size calculation formula and the parameter setting of the proposed accrual model are discussed further.
机译:在固定试验长度的生存试验中,患者应计对试验的力量对样品大小估计或同等的影响有重大影响。交错患者入口需要更大的样品大小。在注册期间,患者的累计率随招聘宣传效应而变化,疾病发病率和许多其他因素和归因率的波动频繁发生。然而,现有的应计模型用于恒定的速率假设或复杂的计算,用于降低应计的补贴。需要更灵活的应计模型来表示用于精确样本量估计的波动患者应计率。在本文中,灵感来自高斯混合分布在近似连续密度的高斯混合分布的灵活性,我们提出了截短的高斯混合分布应合理模型来表示不同参数配置的不同差值变化。进一步讨论了所提出的应计模型的样本量计算公式和参数设置。

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