Mapping functions for the PHQ-9 and GAD-7 to generate EQ-5D-3L for economic evaluation

Clara Mukuria, Matthew Franklin, Sebastian Hinde
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Abstract

Purpose

Generic preferenced-based measures, such as EQ-5D-3L, that are used to estimate quality adjusted life years (QALYs) for economic evaluation are not always available in clinical trials. Predicting EQ-5D-3L values from the commonly used Patient Health Questionnaire 9 (PHQ-9) and Generalised Anxiety Disorder-7 (GAD-7) would allow estimation of QALYs from such trials. The aim was to provide mapping functions to estimate EQ-5D-3L from PHQ-9 and GAD-7 to facilitate economic evaluation.

Methods

Data was drawn from four trials of patients with symptoms of depression testing collaborative care or computerised cognitive behavioural therapy. Patients completed PHQ-9, GAD-7, and EQ-5D-3L at different timepoints. Mapping was undertaken using adjusted limited dependent variable mixture models (ALDVMM), ordinary least squares (OLS), and Tobit models based on PHQ-9, GAD-7 scores or questions, and age to predict EQ-5D-3L utilities. Models were selected based on mean error (ME), mean absolute error (MAE), root mean squared error (RMSE), model goodness of fit, and visual inspection of the predictions.

Results

There were 5583 and 3942 observations for EQ-5D-3L combined with PHQ-9 and GAD-7 respectively. ALDVMM models had low ME ( ≤|0.0018|) and MAE ranging from 0.189 to 0.192, while RMSE was from 0.251 to 0.254 and had better predictions than OLS and Tobit models. ALDVMM models with four components based on PHQ-9 and GAD-7 scores are recommended for estimating EQ-5D-3L utilities.

Conclusions

Recommended mapping functions provide users with an approach to estimate EQ-5D-3L utilities for economic evaluation using PHQ-9, GAD-7, or both scores where they have been used together.

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利用 PHQ-9 和 GAD-7 的映射功能生成用于经济评估的 EQ-5D-3L
目的 临床试验中并不总能获得用于估算经济评价的质量调整生命年(QALYs)的通用首选指标,如 EQ-5D-3L。通过常用的患者健康问卷 9(PHQ-9)和广泛性焦虑症-7(GAD-7)预测 EQ-5D-3L 值,可以估算出此类试验的 QALY。我们的目的是提供映射功能,以便从 PHQ-9 和 GAD-7 估算 EQ-5D-3L 值,从而促进经济评估。方法数据来自四项抑郁症状患者试验,这些患者接受了合作护理或计算机认知行为疗法。患者在不同的时间点填写了 PHQ-9、GAD-7 和 EQ-5D-3L。使用调整后的有限因变量混合模型(ALDVMM)、普通最小二乘法(OLS)和基于 PHQ-9、GAD-7 评分或问题及年龄的 Tobit 模型来预测 EQ-5D-3L 效用。根据平均误差 (ME)、平均绝对误差 (MAE)、均方根误差 (RMSE)、模型拟合度以及预测结果的目测检查来选择模型。ALDVMM 模型的 ME 值(≤|0.0018|)和 MAE 值较低,在 0.189 到 0.192 之间,而 RMSE 值在 0.251 到 0.254 之间,其预测结果优于 OLS 和 Tobit 模型。结论推荐的映射函数为用户提供了一种方法,可使用 PHQ-9、GAD-7 或这两种分数估算 EQ-5D-3L 效用,以进行经济评估。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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