Estimands using the treatment policy strategy for addressing intercurrent events are common in Phase III clinical trials. One estimation approach for this strategy is retrieved dropout whereby observed data following an intercurrent event are used to multiply impute missing data. However, such methods have had issues with variance inflation and model fitting due to data sparsity. This paper introduces likelihood-based versions of these approaches, investigating and comparing their statistical properties to the existing retrieved dropout approaches, simpler analysis models and reference-based multiple imputation. We use a simulation based upon the data from the PIONEER 1 Phase III clinical trial in Type II diabetics to present complex and relevant estimation challenges. The likelihood-based methods display similar statistical properties to their multiple imputation equivalents, but all retrieved dropout approaches suffer from high variance. Retrieved dropout approaches appear less biased than reference-based approaches, resulting in a bias-variance trade-off, but we conclude that the large degree of variance inflation is often more problematic than the bias. Therefore, only the simpler retrieved dropout models appear appropriate as a primary analysis in a clinical trial, and only where it is believed most data following intercurrent events will be observed. The jump-to-reference approach may represent a more promising estimation approach for symptomatic treatments due to its relatively high power and ability to fit in the presence of much missing data, despite its strong assumptions and tendency towards conservative bias. More research is needed to further develop how to estimate the treatment effect for a treatment policy strategy.

翻译：采用治疗策略处理并发事件的估计目标在III期临床试验中较为常见。该策略的一种估计方法为"检索退出法"，即利用并发事件后观察到的数据对缺失数据进行多重插补。然而，此类方法因数据稀疏性存在方差膨胀和模型拟合问题。本文引入这些方法的似然估计版本，探究并比较其与现有检索退出法、简化分析模型及基于参考数据的多重插补法的统计性质。我们基于PIONEER 1型糖尿病III期临床试验数据进行模拟，以呈现复杂且相关的估计挑战。似然方法表现出与多重插补等价方法相似的统计性质，但所有检索退出法均存在高方差问题。检索退出法的偏倚小于基于参考数据的方法，从而形成偏倚-方差权衡，但我们认为高度方差膨胀通常比偏倚更具问题性。因此，仅当确信能观察到大多数并发事件后数据时，较简单的检索退出模型才适合作为临床试验的主要分析方法。对于症状性治疗，"跳跃至参考"方法因其相对较高的检验效能和在大量缺失数据情况下的拟合能力，可能成为更有前景的估计方法——尽管其假设较强且倾向于保守偏倚。针对治疗策略的疗效估计方法仍需进一步研究。