Biostatistics Centre of Expertise Novo Nordisk A/S, Denmark.
Advanced Analytics, Novo Nordisk A/S, Denmark.
J Biopharm Stat. 2022 Nov 2;32(6):942-953. doi: 10.1080/10543406.2022.2058525. Epub 2022 Jun 2.
When dealing with missing data in clinical trials, it is often convenient to work under simplifying assumptions, such as missing at random (MAR), and follow up with sensitivity analyses to address unverifiable missing data assumptions. One such sensitivity analysis, routinely requested by regulatory agencies, is the so-called tipping point analysis, in which the treatment effect is re-evaluated after adding a successively more extreme shift parameter to the predicted values among subjects with missing data. If the shift parameter needed to overturn the conclusion is so extreme that it is considered clinically implausible, then this indicates robustness to missing data assumptions. Tipping point analyses are frequently used in the context of continuous outcome data under multiple imputation. While simple to implement, computation can be cumbersome in the two-way setting where both comparator and active arms are shifted, essentially requiring the evaluation of a two-dimensional grid of models. We describe a computationally efficient approach to performing two-way tipping point analysis in the setting of continuous outcome data with multiple imputation. We show how geometric properties can lead to further simplification when exploring the impact of missing data. Lastly, we propose a novel extension to a multi-way setting which yields simple and general sufficient conditions for robustness to missing data assumptions.
在临床试验中处理缺失数据时，通常方便在简化假设下工作，例如随机缺失（MAR），并进行敏感性分析以解决无法验证的缺失数据假设。监管机构通常会要求进行这样的敏感性分析，即所谓的临界点分析，即在对缺失数据受试者的预测值中添加越来越极端的偏移参数后，重新评估治疗效果。如果需要翻转结论的偏移参数如此极端，以至于被认为在临床上不合理，那么这表明对缺失数据假设具有稳健性。临界点分析常用于多重插补下的连续结局数据。虽然实现起来很简单，但在双向设置中计算可能很繁琐，本质上需要评估模型的二维网格。我们描述了一种在具有多重插补的连续结局数据中进行双向临界点分析的计算效率方法。我们展示了在探索缺失数据的影响时，几何性质如何导致进一步简化。最后，我们提出了一种多向设置的新扩展，为对缺失数据假设的稳健性提供了简单而通用的充分条件。
