[论文解读] Estimation methods for estimands using the treatment policy strategy; a simulation study based on the PIONEER 1 Trial

James Bell, Thomas Drury+7 more|arXiv (Cornell University)|Feb 20, 2024

Health Systems, Economic Evaluations, Quality of Life被引用 5

一句话总结

论文引入基于似然的估计方法用于干预事件中的治疗策略，比较它们与提取性失访、基于参照缺失值插补以及简单模型，使用 PIONEER 1 试验仿真，并讨论方差膨胀与偏差权衡。

ABSTRACT

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.

研究动机与目标

• 为在包含干预事件的 III 期试验中的治疗策略估计方法的评估提供动机。
• 开发与检索型丢失数据方法相对应的基于似然的方法。
• 比较基于似然的方法与检索型丢失数据、基于参照的多重插补以及简单分析模型在统计性质上的差异。
• 在基于 PIONEER 1 II 型糖尿病试验数据的仿真中评估性能。

提出的方法

• 提出治疗策略估计量的检索型丢失数据方法的基于似然的版本。
• 与现有的检索型丢失数据方法、较简单的分析模型以及基于参照的多重插补进行比较。
• 使用以 PIONEER 1 试验数据为基础的仿真研究来评估估计特性。
• 评估各方法的方差膨胀、偏差和统计功效。
• 讨论在干预事件后数据缺失较大时，临床试验主要分析的实际影响。

实验结果

研究问题

• RQ1 基于似然的治疗策略估计量在方差和偏差方面的表现，与检索型丢失数据和基于参照的插补相比如何？
• RQ2 在明显缺失的情况下，检索型丢失数据与跳到参照（jump-to-reference）方法在偏差与方差之间的权衡是什么？
• RQ3 在治疗策略框架下，哪种估计方法在统计功效与鲁棒性之间提供最佳平衡，特别是对症状治疗？
• RQ4 在数据缺失的情况下，跳到参照（jump-to-reference）是否可提供一个可行的替代方案，但其具有特定假设和偏差倾向？

主要发现

• 基于似然的检索方法在统计性质上与其多重插补等价物相似。
• 检索型丢失数据方法表现出较高的方差，常常比偏差更成问题。
• 检索型丢失数据方法的偏差小于基于参照的方法，表明存在偏差-方差权衡。
• 某些方法中的大方差膨胀表明它们可能不适合作为许多试验的主要分析。
• 跳到参照可能提供更高的统计功效和对缺失数据的容忍度，但伴随较强的假设和保守偏差。
• 总体而言，在干预事件后大部分数据被观察到的情况下，建议使用更简单的检索型丢失数据模型作为主要分析。