Design and analysis of two-phase studies with binary outcome applied to Wilms tumour prognosis
Design and analysis of two-phase studies with binary outcome applied to Wilms tumour prognosis
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DOI：
10.1111/1467-9876.00165
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发表时间：
1999-01-01
影响因子：
1.6
通讯作者：
Chatterjee, N
中科院分区：
文献类型：
--
作者：
Breslow, NE;Chatterjee, N
两阶段分层抽样用于选择用于收集额外数据的对象，例如测量误差问题中的验证数据。与仅基于结果或协变量的分层相比，联合按结果和协变量分层，并选择抽样部分，以在第二阶段达到每层大致相等的数量，提高了效率。非参数最大似然法可能比加权或伪似然方法更有效地估计Logistic回归系数。可以使用软件来执行所有这三个程序。我们通过对美国国家肾母细胞瘤研究数据的分析和模拟，证明了这些设计和分析原则的实际重要性。
Two-phase stratified sampling is used to select subjects for the collection of additional data, e.g. validation data in measurement error problems. Stratification jointly by outcome and covariates, with sampling fractions chosen to achieve approximately equal numbers per stratum at the second phase of sampling, enhances efficiency compared with stratification based on the outcome or covariates alone. Nonparametric maximum likelihood may result in substantially more efficient estimates of logistic regression coefficients than weighted or pseudolikelihood procedures. Software to implement all three procedures is available. We demonstrate the practical importance of these design and analysis principles by an analysis of, and simulations based on, data from the US National Wilms Tumor Study.