论文标题
Smim：使用多个插补的统一生存灵敏度分析的统一框架
SMIM: a unified framework of Survival sensitivity analysis using Multiple Imputation and Martingale
论文作者
论文摘要
审查生存数据在临床试验研究中很常见。我们提出了一个统一的敏感性分析框架，以使用多个插补和称为Smim的Martingale在生存数据中随机审查。所提出的框架采用了由灵敏度参数索引的δ调整和基于控制的模型，需要随机审查，并且在不随机假设时进行了广泛的审查集合。此外，考虑到由于审查而导致的数据丢失，它针对广泛的治疗效果估计值定义为治疗特异性生存功能的功能。多次插补有助于使用简单的全样本估计；但是，标准鲁宾的组合规则可能高估了灵敏度分析框架中推断的差异。我们根据估算器的顺序构造将多个插补估计器分解为Martingale系列，并通过重新采样Martingale系列提出了野生引导推断。新的自举推断具有一致性的理论保证，并且与非参数bootstrap对应物相比，计算有效。我们通过模拟和对HIV临床试验的应用评估了所提出的SMIM的有限样本性能。
Censored survival data are common in clinical trial studies. We propose a unified framework for sensitivity analysis to censoring at random in survival data using multiple imputation and martingale, called SMIM. The proposed framework adopts the δ-adjusted and control-based models, indexed by the sensitivity parameter, entailing censoring at random and a wide collection of censoring not at random assumptions. Also, it targets for a broad class of treatment effect estimands defined as functionals of treatment-specific survival functions, taking into account of missing data due to censoring. Multiple imputation facilitates the use of simple full-sample estimation; however, the standard Rubin's combining rule may overestimate the variance for inference in the sensitivity analysis framework. We decompose the multiple imputation estimator into a martingale series based on the sequential construction of the estimator and propose the wild bootstrap inference by resampling the martingale series. The new bootstrap inference has a theoretical guarantee for consistency and is computationally efficient compared to the non-parametric bootstrap counterpart. We evaluate the finite-sample performance of the proposed SMIM through simulation and an application on a HIV clinical trial.
