Health Economics and Decision Science, School of Health and Related Research, University of Sheffield, Sheffield, UK.
BMC Cancer. 2021 Nov 18;21(1):1237. doi: 10.1186/s12885-021-08964-5.
The types of outcomes measured collected in clinical studies and those required for cost-effectiveness analysis often differ. Decision makers routinely use quality adjusted life years (QALYs) to compare the benefits and costs of treatments across different diseases and treatments using a common metric. QALYs can be calculated using preference-based measures (PBMs) such as EQ-5D-3L, but clinical studies often focus on objective clinician or laboratory measured outcomes and non-preference-based patient outcomes, such as QLQ-C30. We model the relationship between the generic, preference-based EQ-5D-3L and the cancer specific quality of life questionnaire, QLQ-C30 in patients with breast cancer. This will result in a mapping that allows users to convert QLQ-C30 scores into EQ-5D-3L scores for the purposes of cost-effectiveness analysis or economic evaluation.
We use data from a randomized trial of 602 patients with HER2-positive advanced breast cancer provided 3766 EQ-5D-3L observations. Direct mapping using adjusted, limited dependent variable mixture models (ALDVMM) is compared to a random effects linear regression and indirect mapping using seemingly unrelated ordered probit models. EQ-5D-3L was estimated as a function of the summary scales of the QLQ-C30 and other patient characteristics.
A four component mixture model outperformed other models in terms of summary fit statistics. A close fit to the observed data was observed across the range of disease severity. Simulated data from the model closely aligned to the original data and showed that mapping did not significantly underestimate uncertainty. In the simulated data, 22.15% were equal to 1 compared to 21.93% in the original data. Variance was 0.0628 in the simulated data versus 0.0693 in the original data. The preferred mapping is provided in Excel and Stata files for the ease of users.
A four component adjusted mixture model provides reliable, non-biased estimates of EQ-5D-3L from the QLQ-C30, to link clinical studies to economic evaluation of health technologies for breast cancer. This work adds to a growing body of literature demonstrating the appropriateness of mixture model based approaches in mapping.
临床研究中测量的结局类型与成本效益分析所需的结局类型往往不同。决策者通常使用质量调整生命年（QALYs）来比较不同疾病和治疗方法的效益和成本，使用共同的指标。QALYs 可以使用偏好量表（PBMs）进行计算，如 EQ-5D-3L，但临床研究通常侧重于客观的临床医生或实验室测量的结局以及非偏好的患者结局，如 QLQ-C30。我们对乳腺癌患者的通用偏好量表 EQ-5D-3L 和癌症特异性生活质量问卷 QLQ-C30 之间的关系进行建模。这将产生一个映射，允许用户将 QLQ-C30 评分转换为 EQ-5D-3L 评分，用于成本效益分析或经济评估。
我们使用来自 602 例 HER2 阳性晚期乳腺癌患者随机试验的数据，共提供了 3766 个 EQ-5D-3L 观察值。使用调整后的有限依存变量混合模型（ALDVMM）进行直接映射，并与随机效应线性回归和间接映射（使用似乎不相关的有序概率模型）进行比较。EQ-5D-3L 被估计为 QLQ-C30 的总结量表和其他患者特征的函数。
在综合拟合统计方面，四分量混合模型优于其他模型。在疾病严重程度的范围内观察到与观察数据的紧密拟合。来自模型的模拟数据与原始数据非常吻合，并表明映射不会显著低估不确定性。在模拟数据中，22.15%等于 1，而在原始数据中为 21.93%。在模拟数据中，方差为 0.0628，而在原始数据中为 0.0693。为了方便用户，我们在 Excel 和 Stata 文件中提供了首选映射。
四分量调整混合模型为将临床研究与乳腺癌卫生技术的经济评估联系起来，从 QLQ-C30 中提供了 EQ-5D-3L 的可靠、无偏估计。这项工作增加了越来越多的文献，证明了基于混合模型的方法在映射中的适当性。
