In dependently censored survival data, the usual assumption of independent censoring or an incorrect specification of the correlation between the event and censoring times can bias marginal survival inference. Likelihood-based estimation of this dependence can be numerically unstable with large variance, and practical alternatives are limited. The proposed method uses generalized method-of-moments to estimate the copula correlation parameter of a Normal, Clayton, Gumbel, or Frank copula that links exponential, Weibull, or log-normal marginal survival times. Bootstrap-aggregation of simulated annealing is employed over candidate correlation ranges to obtain stable estimates. Simulations assess accuracy and uncertainty via mean absolute error, bootstrap confidence intervals, and empirical coverage. The method is applied to a double-blind randomized clinical trial with dependent censoring from early patient dropouts, where accurate marginal survival inference is needed to estimate the effect of a treatment on patient survival.

翻译：在存在依赖删失的生存数据中，通常假设的独立删失或对事件时间与删失时间之间相关性的错误设定可能导致边际生存推断出现偏倚。基于似然的该依赖性估计在数值上可能不稳定且方差较大，而实际可行的替代方法有限。所提出的方法采用广义矩估计来估计连接指数、威布尔或对数正态边际生存时间的正态、Clayton、Gumbel或Frank copula的相关参数。通过自举聚合模拟退火算法，在候选相关范围内获得稳定估计。模拟研究通过平均绝对误差、自举置信区间和经验覆盖率评估了准确性与不确定性。该方法被应用于一项存在早期患者退出导致的依赖删失的双盲随机对照临床试验，其中需要准确的边际生存推断以估计治疗对患者生存的影响。