研究人员考虑利用外部来源的信息来加强小规模研究的分析变得越来越普遍。虽然很多注意力都集中在单变量生存数据上，但相关生存数据在流行病学调查中也很普遍。在本文中，我们提出了一个统一的框架，通过整合以减少的加速失效时间模型中评估的协变量效应形式给出的附加信息，改进对具有相关生存数据的边际加速失效时间模型的估计。这些辅助信息可以通过使用有效的估计方程来概括，因此可以通过广义矩方法与内部线性秩估计方程相结合。我们研究了所提出的估计器的渐近特性，并表明它比仅使用内部数据的传统估计器更有效。当存在群体异质性时，我们修改所提出的估计程序并提出收缩估计器以防止偏差和效率损失。此外，所提出的估计过程可以进一步细化，以适应辅助信息中不可忽略的不确定性，从而得出更可信的推理结论。模拟结果证明了所提出方法的有限样本性能，前列腺癌、肺癌、结直肠癌和卵巢癌筛查试验的实证应用证实了其实际相关性。© 2024 John Wiley

It is becoming increasingly common for researchers to consider leveraging information from external sources to enhance the analysis of small-scale studies. While much attention has focused on univariate survival data, correlated survival data are prevalent in epidemiological investigations. In this article, we propose a unified framework to improve the estimation of the marginal accelerated failure time model with correlated survival data by integrating additional information given in the form of covariate effects evaluated in a reduced accelerated failure time model. Such auxiliary information can be summarized by using valid estimating equations and hence can then be combined with the internal linear rank-estimating equations via the generalized method of moments. We investigate the asymptotic properties of the proposed estimator and show that it is more efficient than the conventional estimator using internal data only. When population heterogeneity exists, we revise the proposed estimation procedure and present a shrinkage estimator to protect against bias and loss of efficiency. Moreover, the proposed estimation procedure can be further refined to accommodate the non-negligible uncertainty in the auxiliary information, leading to more trustable inference conclusions. Simulation results demonstrate the finite sample performance of the proposed methods, and empirical application on the Prostate, Lung, Colorectal, and Ovarian Cancer Screening Trial substantiates its practical relevance.© 2024 John Wiley & Sons Ltd.