Using Instrument-Defined Health State Transitions to Estimate Minimally Important Differences for Four Preference-Based Health-Related Quality of Life Instruments
Using Instrument-Defined Health State Transitions to Estimate Minimally Important Differences for Four Preference-Based Health-Related Quality of Life Instruments
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DOI：
10.1097/mlr.0b013e3181c162a2
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发表时间：
2010-04-01
期刊：
影响因子：
3
通讯作者：
Coons, Stephen Joel
中科院分区：
文献类型：
--
作者：
Luo, Nan;Johnson, Jeffrey A.;Coons, Stephen Joel
目的：利用每个工具的多属性健康分类（MAHC）系统定义的健康状态转换，估计EQ-5D、健康公用事业指数标记II （HUI2）、HUI3和SF-6D健康指数得分的最小重要差异（MIDs）。方法：我们假设与MAHC系统定义的最小健康转变相关的偏好得分的变化是最小重要的。由MAHC系统定义的两个健康状态之间的任何转换，如果只有一个健康维度或属性不同，并且只有一个功能级别不同，则被视为“最小健康转换”。因此，每个这样的健康转换提供1个MID估计。使用其MAHC系统定义的所有假设的最小健康转变来估计4种仪器中每种仪器的MID。结果：根据我们的定义，EQ-5D的最小健康转变总数为405，HUI2为127,600，HUI3为6,382,800，SF-6D为86,700。EQ-5D （US算法）的平均（标准偏差）MID估计为0.040 (0.026),EQ-5D （UK算法）为0.082 (0.032)，HUI2为0.045 (0.039)，HUI3为0.032 (0.027)，SF-6D为0.027（0.028）。这些MID估计的效应量在0.11到0.37之间。这些MID估计值与使用基于锚点的方法从经验数据估计的已发表值相当。结论：可以使用MAHC系统定义的健康转变来估计基于偏好的健康指数得分的mid。本研究提供了关于所检查的4个健康指数的MID估计的新信息。
Objective: To estimate minimally important differences (MIDs) for the EQ-5D, Health Utilities Index Mark II (HUI2), HUI3, and SF-6D health index scores using health-state transitions defined by each instrument's multiattribute health classification (MAHC) system.Methods: We assume that changes in preference scores associated with the smallest health transitions defined by an MAHC system are minimally important. Any transitions between 2 health states defined by an MAHC system which differ in only one health dimension or attribute and by only one functional level are considered "smallest health transitions." Thus, each such health transition provides 1 MID estimate. The MID for each of the 4 instruments was estimated using all the hypothetical smallest health transitions defined by its MAHC system.Results: Based on our definitions, the total number of smallest health transitions was 405 for the EQ-5D, 127,600 for the HUI2, 6,382,800 for the HUI3, and 86,700 for the SF-6D. The mean (standard deviation) MID estimate was 0.040 (0.026) for the EQ-5D (US algorithm), 0.082 (0.032) for the EQ-5D (UK algorithm), 0.045 (0.039) for the HUI2, 0.032 (0.027) for the HUI3, and 0.027 (0.028) for the SF-6D. The effect sizes of these MID estimates ranged from 0.11 to 0.37. These MID estimates are quite comparable to published values estimated from empirical data using anchor-based methods.Conclusions: It is possible to use health transitions defined by the MAHC system to estimate the MIDs for preference-based health index scores. This study provides new information regarding MID estimates for the 4 health indices examined.