Yu Jichang, Liu Yanyan, Cai Jianwen, Sandler Dale P, Zhou Haibo
School of Statistics and Mathematics, Zhongnan University of Economics and Law, Wuhan, Hubei 430073, China; School of Mathematics and Statistics, Wuhan University, Wuhan, Hubei 430072, China.
School of Mathematics and Statistics, Wuhan University, Wuhan, Hubei 430072, China.
J Stat Plan Inference. 2016 Nov;178:24-36. doi: 10.1016/j.jspi.2016.05.001. Epub 2016 May 17.
We propose a cost-effective outcome-dependent sampling design for the failure time data and develop an efficient inference procedure for data collected with this design. To account for the biased sampling scheme, we derive estimators from a weighted partial likelihood estimating equation. The proposed estimators for regression parameters are shown to be consistent and asymptotically normally distributed. A criteria that can be used to optimally implement the ODS design in practice is proposed and studied. The small sample performance of the proposed method is evaluated by simulation studies. The proposed design and inference procedure is shown to be statistically more powerful than existing alternative designs with the same sample sizes. We illustrate the proposed method with an existing real data from the Cancer Incidence and Mortality of Uranium Miners Study.
我们针对失效时间数据提出了一种具有成本效益的基于结果的抽样设计，并为采用该设计收集的数据开发了一种有效的推断程序。为了考虑有偏抽样方案，我们从加权部分似然估计方程中推导出估计量。所提出的回归参数估计量被证明是一致的且渐近正态分布。提出并研究了一种可用于在实践中最优实施ODS设计的准则。通过模拟研究评估了所提方法的小样本性能。结果表明，在所使用样本量相同的情况下，所提出的设计和推断程序在统计上比现有的替代设计更具效力。我们用铀矿工癌症发病率和死亡率研究中的一个现有真实数据说明了所提方法。
