A unified approach to sample size and power determination for testing parameters in generalized linear and time-to-event regression models.
A unified approach to sample size and power determination for testing parameters in generalized linear and time-to-event regression models.
复制标题
广义线性和时间事件回归模型中检验参数的样本大小和功效确定的统一方法。
DOI：
10.1002/sim.8823
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发表时间：
2021-02-28
影响因子：
2
通讯作者：
Logan BR
中科院分区：
文献类型：
--
作者：
Martens MJ;Logan BR
为了确保一项研究能够正确地解决其研究目标，必须适当地确定样本量和力量。通过回归建模的协变量调整允许更精确地估计感兴趣的主要变量的影响，但代价是增加了样本量/幂计算的复杂性。在观察性研究和非随机临床试验中常见的主变量和其他协变量之间的相关性的存在，使这一过程进一步复杂化。虽然已经获得了适应特定协变量分布和模型的样本量和功率指定方法，但大多数现有方法要么依赖于缺乏理论支持的简单近似，要么依赖于难以在设计阶段应用的复杂程序。目前的文献缺乏适用于更广泛类别的回归模型和协变量分布的一般的、连贯的理论。我们介绍了用广义线性模型、COX模型和Fine-Gray模型来决定样本大小和力量的简明公式，这些模型解释了主效应和其他协变量之间的相关性。大量的模拟表明，这种方法产生的研究规模适当，以满足其I类错误率和功率规格，特别是在存在相关协变量的情况下提供准确的样本量/功率估计。
To ensure that a study can properly address its research aims, the sample size and power must be determined appropriately. Covariate adjustment via regression modeling permits more precise estimation of the effect of a primary variable of interest at the expense of increased complexity in sample size / power calculation. The presence of correlation between the main variable and other covariates, commonly seen in observational studies and non-randomized clinical trials, further complicates this process. Though sample size and power specification methods have been obtained to accommodate specific covariate distributions and models, most existing approaches rely on either simple approximations lacking theoretical support or complex procedures that are difficult to apply at the design stage. The current literature lacks a general, coherent theory applicable to a broader class of regression models and covariate distributions. We introduce succinct formulas for sample size and power determination with the generalized linear, Cox, and Fine-Gray models that account for correlation between a main effect and other covariates. Extensive simulations demonstrate that this method produces studies that are appropriately sized to meet their type I error rate and power specifications, particularly offering accurate sample size/power estimation in the presence of correlated covariates.
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影响因子：
2
作者：
Tosteson, TD;Buzas, JS;Karagas, M
通讯作者：
Karagas, M
影响因子：
20.3
作者：
Abu Zaid, Mohammad;Wu, Juan;Choi, Sung Won
通讯作者：
Choi, Sung Won
影响因子：
2
作者：
Gilbert PB;Janes HE;Huang Y
通讯作者：
Huang Y
DOI：
10.1016/s0197-2456(00)00104-5
发表时间：
2000-12-01
期刊：
CONTROLLED CLINICAL TRIALS
影响因子：
--
作者：
Hsieh, FY;Lavori, PW
通讯作者：
Lavori, PW
影响因子：
1.9
作者：
SELF, SG;MAURITSEN, RH
通讯作者：
MAURITSEN, RH