Department of Statistics, North Carolina State University, Raleigh, North Carolina, USA.
Merck & Co., Inc., Kenilworth, New Jersey, USA.
Biometrics. 2023 Mar;79(1):230-240. doi: 10.1111/biom.13555. Epub 2021 Sep 20.
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 a broad class of treatment effect estimands defined as functionals of treatment-specific survival functions, taking into account 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 nonparametric bootstrap counterpart. We evaluate the finite-sample performance of the proposed SMIM through simulation and an application on an HIV clinical trial.
删失生存数据在临床试验研究中很常见。我们提出了一种使用多重插补和鞅的随机删失敏感性分析的统一框架，称为 SMIM。所提出的框架采用了 δ 调整和基于控制的模型，由敏感性参数索引，涉及随机删失和广泛的非随机删失假设。此外，它针对由于删失而导致的缺失数据，针对作为治疗特异性生存函数的函数的广泛的治疗效果估计量。多重插补便于使用简单的全样本估计；然而，标准的鲁宾合并规则可能会高估敏感性分析框架中推断的方差。我们基于估计量的顺序构建将多重插补估计量分解为鞅级数，并通过对鞅级数进行重采样提出了野 Bootstrap 推断。新的自举推断在理论上保证了一致性，并且与非参数自举相比具有计算效率。我们通过模拟和对 HIV 临床试验的应用来评估所提出的 SMIM 的有限样本性能。
