Outcome-dependent sampling designs such as the case–control or case–cohort design are widely used in epidemiological studies for their outstanding cost-effectiveness. In this article, we propose and develop a smoothed weighted Gehan estimating equation approach for inference in an accelerated failure time model under a general failure time outcome-dependent sampling scheme. The proposed estimating equation is continuously differentiable and can be solved by the standard numerical methods. In addition to developing asymptotic properties of the proposed estimator, we also propose and investigate a new optimal power-based subsamples allocation criteria in the proposed design by maximizing the power function of a significant test. Simulation results show that the proposed estimator is more efficient than other existing competing estimators and the optimal power-based subsamples allocation will provide an ODS design that yield improved power for the test of exposure effect. We illustrate the proposed method with a data set from the Norwegian Mother and Child Cohort Study to evaluate the relationship between exposure to perfluoroalkyl substances and women’s subfecundity.

病例对照或病例队列设计等结果相关抽样设计因其出色的成本效益而广泛应用于流行病学研究。在本文中，我们提出并开发了一种平滑加权 Gehan 估计方程方法，用于在一般故障时间结果相关采样方案下的加速故障时间模型中进行推理。所提出的估计方程是连续可微的，并且可以通过标准数值方法求解。除了开发所提出的估计器的渐近特性之外，我们还通过最大化显着性检验的幂函数，在所提出的设计中提出并研究了一种新的基于幂的最佳子样本分配标准。仿真结果表明，所提出的估计器比其他现有的竞争估计器更有效，并且基于最佳功率的子样本分配将提供 ODS 设计，为暴露效果测试提供改进的功率。我们用挪威母婴队列研究的数据集说明了所提出的方法，以评估接触全氟烷基物质与女性生育力低下之间的关系。