Background

Health state utility values (‘utilities’) are an integral part of health technology assessment. Though traditionally categorised by disease status in oncology (i.e. progression), several recent assessments have adopted values calculated according to the time that measures were recorded before death. We conducted a simulation study to understand the limitations of each approach, with a focus on mismatches between the way utilities are generated, and analysed.

Methods

Survival times were simulated based on published literature, with permutations of three utility generation mechanisms (UGMs) and utility analysis methods (UAMs): (1) progression based, (2) time-to-death based, and (3) a ‘combination approach’. For each analysis quality-adjusted life-years (QALYs) were estimated. Goodness of fit was assessed via percentage mean error (%ME) and mean absolute error (%MAE). Scenario analyses were performed varying individual parameters, with complex scenarios mimicking published studies. The statistical code is provided for transparency and to aid future work in the area.

Results

%ME and %MAE were lowest when the correct analysis form was specified (i.e. UGM and UAM aligned). Underestimates were produced when a time-to-death element was present in the UGM but not included in the UAM, while the ‘combined’ UAM produced overestimates irrespective of the UGM. Scenario analysis demonstrated the importance of the volume of available data beyond the initial time period, for example follow-up.

Conclusions

We show that the use of an incorrectly or over-specified UAM can result in substantial bias in the estimation of utilities. We present a flowchart to highlight the issues that may be faced.

中文翻译：

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用于健康状态效用值的分析方法对肿瘤学中的 QALYs 有何影响？比较基于进程的方法和死亡时间方法的模拟研究

背景

健康状态效用值（“效用”）是卫生技术评估的一个组成部分。尽管传统上按肿瘤学中的疾病状态（即进展）进行分类，但最近的一些评估采用了根据死亡前记录措施的时间计算的值。我们进行了一项模拟研究以了解每种方法的局限性，重点是公用事业生成和分析方式之间的不匹配。

方法

生存时间是根据已发表的文献模拟的，具有三种效用生成机制 (UGM) 和效用分析方法 (UAM) 的排列：（1）基于进展，（2）基于死亡时间，以及（3）“组合”方法'。对于每次分析，估计了质量调整生命年 (QALY)。拟合优度通过百分比平均误差 (%ME) 和平均绝对误差 (%MAE) 进行评估。情景分析是根据不同的个体参数进行的，复杂的情景模拟了已发表的研究。提供统计代码是为了提高透明度并有助于该领域的未来工作。

结果

当指定正确的分析形式（即 UGM 和 UAM 对齐）时，%ME 和 %MAE 最低。当 UGM 中存在死亡时间元素但未包含在 UAM 中时，会产生低估，而“组合”UAM 产生高估，而与 UGM 无关。情景分析证明了初始时间段之外的可用数据量的重要性，例如后续行动。

结论

我们表明，使用不正确或过度指定的 UAM 会导致对效用估计的重大偏差。我们提供了一个流程图来突出可能面临的问题。