Department of Statistics, University of Connecticut, CT, USA.
Department of Statistics, University of Connecticut, CT, USA.
Value Health. 2020 Dec;23(12):1622-1629. doi: 10.1016/j.jval.2020.09.002. Epub 2020 Oct 29.
To develop a simple approach for evaluating the overall benefit-risk of a new noninferiority treatment compared with a standard of care.
We propose using multicriteria decision analysis that accounts for uncertainty associated with both clinical outcomes and patient preference data. Because patients' preferences are likely to be influenced by their baseline characteristics, we suggest carrying out a preference study at the beginning of a trial. To reduce the burden of an additional study questionnaire, preference elicitation could be done on a small sample of trial participants. To restore preferences for all trial participants, we propose using multiple imputation (MI). Using simulations, we examine whether 3 different MI procedures lead to the same benefit-risk assessment conclusion, as if all trial participant preferences were obtained. We also compare MI results to complete case analysis, where only preferences of the small sample of trial participants are considered.
We show that the MI procedure successfully restores patients' preferences for the trial participants using different outcome criteria and preferences. For example, using 3 outcome criteria with only 10% of the trial participants providing their preferences, complete case analysis demonstrated a new noninferior treatment as favorable only 5.1% of the time, whereas MI procedures did so between 16.2% and 17.9% of the time. Given that 17.6% correspond to the fully observed weights, the MI methods demonstrate favorable results.
The MI procedure can help facilitate a simple comprehensive benefit-risk assessment for new noninferior treatments.
开发一种简单的方法来评估新的非劣效治疗与标准治疗相比的总体获益-风险。
我们建议使用多准则决策分析，该分析考虑了与临床结局和患者偏好数据相关的不确定性。由于患者的偏好可能受到其基线特征的影响，我们建议在试验开始时进行偏好研究。为了降低额外研究问卷的负担，可以在试验参与者的小样本中进行偏好 elicitation。为了恢复所有试验参与者的偏好，我们建议使用多重插补（MI）。通过模拟，我们检查了 3 种不同的 MI 程序是否会导致相同的获益-风险评估结论，就好像所有试验参与者的偏好都得到了一样。我们还将 MI 结果与完全案例分析进行了比较，其中仅考虑了试验参与者小样本的偏好。
我们表明，MI 程序可以使用不同的结局标准和偏好成功地恢复试验参与者的偏好。例如，使用 3 个结局标准，只有 10%的试验参与者提供了他们的偏好，完全案例分析显示新的非劣效治疗只有 5.1%的时间是有利的，而 MI 程序则有 16.2%至 17.9%的时间是有利的。鉴于 17.6%对应于完全观察到的权重，MI 方法显示出有利的结果。
MI 程序可以帮助促进新的非劣效治疗的简单综合获益-风险评估。
