The primary analysis of time-to-event data typically makes the censoring at random assumption, that is, that—conditional on covariates in the model—the distribution of event times is the same, whether they are observed or unobserved. In such cases, we need to explore the robustness of inference to more pragmatic assumptions about patients post-censoring in sensitivity analyses. Reference-based multiple imputation, which avoids analysts explicitly specifying the parameters of the unobserved data distribution, has proved attractive to researchers. Building on results for longitudinal continuous data, we show that inference using a Tobit regression imputation model for reference-based sensitivity analysis with right censored log normal data is information anchored, meaning the proportion of information lost due to missing data under the primary analysis is held constant across the sensitivity analyses. We illustrate our theoretical results using simulation and a clinical trial case study.

中文翻译：

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用于生存分析的截断正常数据的基于信息锚定参考的敏感性分析

对事件时间数据的主要分析通常进行随机审查，即——以模型中的协变量为条件——事件时间的分布是相同的，无论它们是观察到的还是未观察到的。在这种情况下，我们需要探索对敏感性分析中关于患者后删失的更务实假设的推断的稳健性。基于参考的多重插补避免了分析师明确指定未观察到的数据分布的参数，已证明对研究人员很有吸引力。基于纵向连续数据的结果，我们表明使用 Tobit 回归插补模型进行基于右删失对数正态数据的基于参考的敏感性分析的推断是信息锚定的，这意味着在主要分析下由于缺失数据而丢失的信息比例在整个敏感性分析中保持不变。我们使用模拟和临床试验案例研究来说明我们的理论结果。