Maximum likelihood estimation of logistic regression parameters under two-phase, outcome-dependent sampling
Maximum likelihood estimation of logistic regression parameters under two-phase, outcome-dependent sampling
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DOI：
10.1111/1467-9868.00078
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发表时间：
1997-01-01
期刊：
影响因子：
--
通讯作者：
Holubkov, R
中科院分区：
文献类型：
--
作者：
Breslow, NE;Holubkov, R
结果依赖抽样提高了罕见结果研究的效率，例如流行病学中的病例对照研究和计量经济学中的基于选择的抽样。两相或双采样是一种标准的技术，以绘制有效的分层样品。我们开发的逻辑回归系数的最大似然估计混合两阶段，结果相关的抽样设计。给出了一种通过重复拟合普通逻辑回归模型来确定估计的算法。仿真结果表明，对于特定的数据配置，采用伪似然和加权似然方法会导致效率损失。这些结果为基于离散代理的验证抽样测量误差问题提供了一种有效的解决方案。
Outcome-dependent sampling increases the efficiency of studies of rare outcomes, examples being case-control studies in epidemiology and choice-based sampling in econometrics. Two-phase or double sampling is a standard technique for drawing efficient stratified samples. We develop maximum likelihood estimation of logistic regression coefficients for a hybrid two-phase, outcome-dependent sampling design. An algorithm is given for determining the estimates by repeated fitting of ordinary logistic regression models. Simulation results demonstrate the efficiency loss associated with alternative pseudolikelihood and weighted likelihood methods for certain data configurations. These results provide an efficient solution to the measurement error problem with validation sampling based on a discrete surrogate.