标题： 基于参考的填补和转折点分析的因果建模框架 显示英文标题

标题： A causal modelling framework for reference-based imputation and tipping point analysis

摘要： 我们考虑在随机对照安慰剂组或标准治疗组的药物试验中估计定量结局的“实际”或有效性估计量，其中停止使用试验性治疗的参与者之后不再随访。 Carpenter 等人（2013）提出了基于参考组的插补方法，该方法利用参考组来推断停药后的结局分布，从而推导出插补模型。 然而，这些基于参考组的插补方法并未得到正式的理论支持。 我们提出了一种因果模型，在潜在结果框架下明确假设了停药后维持的治疗因果效应。 我们证明了以对照组作为参考组的“跳跃至参考组”、“复制参考组”和“复制参考组增量”的基于参考组的插补方法是该因果模型的特例，具有特定的关于因果治疗效应的假设。 我们展示了模拟研究的结果。 我们还表明，该因果模型提供了一个灵活且透明的框架，用于敏感性分析中的转折点分析，其中我们改变对停药治疗因果效应的假设。 我们用两个纵向临床试验的数据来说明这种方法。 显示英文摘要

摘要： We consider estimating the "de facto" or effectiveness estimand in a randomised placebo-controlled or standard-of-care-controlled drug trial with quantitative outcome, where participants who discontinue an investigational treatment are not followed up thereafter. Carpenter et al (2013) proposed reference-based imputation methods which use a reference arm to inform the distribution of post-discontinuation outcomes and hence to inform an imputation model. However, the reference-based imputation methods were not formally justified. We present a causal model which makes an explicit assumption in a potential outcomes framework about the maintained causal effect of treatment after discontinuation. We show that the "jump to reference", "copy reference" and "copy increments in reference" reference-based imputation methods, with the control arm as the reference arm, are special cases of the causal model with specific assumptions about the causal treatment effect. Results from simulation studies are presented. We also show that the causal model provides a flexible and transparent framework for a tipping point sensitivity analysis in which we vary the assumptions made about the causal effect of discontinued treatment. We illustrate the approach with data from two longitudinal clinical trials.