Discipline of Social and Administrative Pharmacy, School of Pharmaceutical Sciences, Universiti Sains Malaysia, Penang, Malaysia; Seri Manjung Hospital, Ministry of Health Malaysia, Seri Manjung, Malaysia.
Discipline of Social and Administrative Pharmacy, School of Pharmaceutical Sciences, Universiti Sains Malaysia, Penang, Malaysia.
Value Health. 2024 Dec;27(12):1762-1770. doi: 10.1016/j.jval.2024.07.016. Epub 2024 Aug 9.
The Assessment of Quality of Life - 6 Dimensions (AQoL-6D), a generic preference-based measure, is an appealing alternative to EQ-5D-5L for assessing health status in patients with chronic heart failure (HF), given its expanded scope. However, without a Malaysian value set, the AQoL-6D cannot generate health state utility values (HSUVs) to support local economic evaluations. This study intended to develop algorithms for predicting EQ-5D-5L HSUVs from AQoL-6D in an HF population.
Cross-sectional data from a multicenter cohort of 419 HF outpatients were used. Both direct and indirect mapping approaches were attempted using 5 sets of explanatory variables and 8 models (ordinary least squares, Tobit, censored least absolute deviations, generalized linear model, 2-part model [TPM], beta regression-based model, adjusted limited dependent variable mixture model, and multinomial ordinal regression [MLOGIT]). The models' predictive performance was assessed through 10-fold cross-validated mean absolute error [MAE] and root mean squared error [RMSE]). Potential prediction bias was also examined graphically. The best-performing models, with the lowest RMSE and no bias, were then identified.
Among the models evaluated, TPM, which included age, sex, and 5 AQoL-6D dimension scores as predictors, appears to be the best-performing model for directly predicting EQ-5D-5L HSUVs from AQoL-6D. TPM yielded the lowest MAE (0.0802) and RMSE (0.1116), and demonstrated predictive accuracy for HSUVs >0.2 without significant bias. A MLOGIT model developed for response mapping had suboptimal predictive accuracy.
This study developed potentially useful mapping algorithms for generating Malaysian EQ-5D-5L HSUVs from AQoL-6D responses among patients with HF when direct EQ-5D-5L data are unavailable.
生活质量评估-6 维度（AQoL-6D）是一种通用的偏好量表，与 EQ-5D-5L 相比，它在评估慢性心力衰竭（HF）患者的健康状况时具有更广泛的范围，因此是一种有吸引力的替代方法。然而，由于缺乏马来西亚价值体系，AQoL-6D 无法生成健康状态效用值（HSUVs），无法支持当地的经济评估。本研究旨在为 HF 人群开发从 AQoL-6D 预测 EQ-5D-5L HSUV 的算法。
使用来自多中心队列的 419 名 HF 门诊患者的横断面数据。尝试使用 5 组解释变量和 8 种模型（普通最小二乘法、Tobit、截尾最小绝对偏差、广义线性模型、两部分模型[TPM]、基于贝塔回归的模型、调整受限因变量混合模型和多项有序回归[MLOGIT]）进行直接和间接映射。通过 10 倍交叉验证平均绝对误差[MAE]和均方根误差[RMSE]评估模型的预测性能。还通过图形检查潜在的预测偏差。然后确定具有最低 RMSE 和无偏差的表现最佳的模型。
在所评估的模型中，包含年龄、性别和 5 个 AQoL-6D 维度得分作为预测因子的 TPM 似乎是从 AQoL-6D 直接预测 EQ-5D-5L HSUV 的表现最佳模型。TPM 产生的 MAE（0.0802）和 RMSE（0.1116）最低，并且对于 HSUVs>0.2 具有预测准确性，没有明显的偏差。为响应映射开发的 MLOGIT 模型预测准确性较差。
当无法直接获得 EQ-5D-5L 数据时，本研究为 HF 患者从 AQoL-6D 反应生成马来西亚 EQ-5D-5L HSUV 开发了潜在有用的映射算法。
