Researchers working on large cohort studies are always seeking for cost-effective designs due to a limited budget. An outcome-dependent sampling (ODS) design, a retrospective sampling scheme where one observes covariates with a probability depending on the outcome and selects supplemental samples from more informative segments, improves the study efficiency while effectively controlling for the budget. To take the advantage of the ODS scheme when multivariate failure times are main response variables, relevant study designs and inference procedures need to be studied. In this paper, we consider a general multivariate-ODS design for multivariate failure times under the framework of a semiparametric accelerated failure time model. We develop a weighted estimating equations approach, based on the induced smoothing method, for parameter estimation. Extensive simulation studies show that our proposed design and estimator are more efficient than other competing estimators based on simple random samples. The proposed method is illustrated with a real data set from the Busselton Health Study.

中文翻译：

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多变量结果相关抽样设计下多变量失效时间的半参数加速失效时间建模

由于预算有限，从事大型队列研究的研究人员一直在寻求具有成本效益的设计。结果依赖抽样 (ODS) 设计，一种回顾性抽样方案，观察协变量的概率取决于结果，并从更多信息的部分中选择补充样本，在有效控制预算的同时提高了研究效率。当多变量失效时间是主要响应变量时，为了利用 ODS 方案，需要研究相关的研究设计和推理程序。在本文中，我们在半参数加速故障时间模型的框架下考虑了用于多元故障时间的通用多元 ODS 设计。我们开发了一种基于诱导平滑方法的加权估计方程方法，用于参数估计。广泛的模拟研究表明，我们提出的设计和估计器比其他基于简单随机样本的竞争估计器更有效。所提出的方法用来自巴瑟尔顿健康研究的真实数据集进行了说明。