Scheike Thomas H, Juul Anders
Department of Biostatistics, University of Copenhagen, Blegdamsvej 3, DK-2200 KBH N, Denmark.
Biostatistics. 2004 Apr;5(2):193-206. doi: 10.1093/biostatistics/5.2.193.
Nested case-control sampling is designed to reduce the costs of large cohort studies. It is important to estimate the parameters of interest as efficiently as possible. We present a new maximum likelihood estimator (MLE) for nested case-control sampling in the context of Cox's proportional hazards model. The MLE is computed by the EM-algorithm, which is easy to implement in the proportional hazards setting. Standard errors are estimated by a numerical profile likelihood approach based on EM aided differentiation. The work was motivated by a nested case-control study that hypothesized that insulin-like growth factor I was associated with ischemic heart disease. The study was based on a population of 3784 Danes and 231 cases of ischemic heart disease where controls were matched on age and gender. We illustrate the use of the MLE for these data and show how the maximum likelihood framework can be used to obtain information additional to the relative risk estimates of covariates.
巢式病例对照抽样旨在降低大型队列研究的成本。尽可能高效地估计感兴趣的参数非常重要。我们在Cox比例风险模型的背景下提出了一种用于巢式病例对照抽样的新的最大似然估计器（MLE）。MLE通过期望最大化（EM）算法计算，该算法在比例风险设定中易于实现。标准误通过基于EM辅助微分的数值轮廓似然方法进行估计。这项工作的动机来自一项巢式病例对照研究，该研究假设胰岛素样生长因子I与缺血性心脏病有关。该研究基于3784名丹麦人的人群和231例缺血性心脏病病例，对照在年龄和性别上进行了匹配。我们说明了MLE在这些数据中的应用，并展示了如何使用最大似然框架来获取协变量相对风险估计之外的信息。
