MSIS-29对瑞典多发性硬化症患者EQ-5D的外部效度分析

IF 1.7
MDM policy & practice Pub Date : 2017-02-01 eCollection Date: 2017-01-01 DOI:10.1177/2381468317692806
Olivia Ernstsson, Petter Tinghög, Kristina Alexanderson, Jan Hillert, Kristina Burström
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引用次数: 4

摘要

背景:当没有基于偏好的测量方法时,可以执行映射来预测特定于条件的测量方法的效用值。先前开发的一种预测多发性硬化症影响量表(MSIS-29) EQ-5D-3L指数值的算法尚未得到外部验证。目的:通过测试瑞典多发性硬化症(MS)患者中MSIS-29预测EQ-5D-3L指数值的准确性,来检验先前开发的映射算法的外部有效性。方法:从2011年至2014年以人口为基础的瑞典登记册中收集横断面个人数据。通过MSIS-29和EQ-5D- 3l对767名已知ms残疾水平的个体在某一时间点的健康相关生活质量进行评估。采用先前开发的映射算法从MSIS-29项目预测EQ-5D指标值,并通过平均绝对误差和均方根误差评估预测准确性。结果:应用该算法时,EQ-5D-3L预测平均指数值为0.77,而实际观测平均指数值为0.75。结论:映射算法在两个独立样本中具有相似的预测精度,但结果显示,在健康状况较好的群体中预测性能最高。不同亚组的预测准确性与先前的研究一致,处理这一问题的策略是必要的。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

The External Validity of Mapping MSIS-29 on EQ-5D Among Individuals With Multiple Sclerosis in Sweden.

The External Validity of Mapping MSIS-29 on EQ-5D Among Individuals With Multiple Sclerosis in Sweden.

The External Validity of Mapping MSIS-29 on EQ-5D Among Individuals With Multiple Sclerosis in Sweden.

The External Validity of Mapping MSIS-29 on EQ-5D Among Individuals With Multiple Sclerosis in Sweden.

Background: Mapping can be performed to predict utility values from condition-specific measures when preference-based measures are absent. A previously developed algorithm that predicts EQ-5D-3L index values from the Multiple Sclerosis Impact Scale (MSIS-29) has not yet been externally validated. Aim: To examine the external validity of a previously developed mapping algorithm by testing the accuracy of predicting EQ-5D-3L index values from MSIS-29 among multiple sclerosis (MS) patients in Sweden. Methods: Cross-sectional individual-level data were collected from population-based Swedish registers between 2011 and 2014. Health-related quality of life was assessed through MSIS-29 and EQ-5D-3L at one point in time among 767 individuals with known disability level of MS. A previously developed mapping algorithm was applied to predict EQ-5D index values from MSIS-29 items, and the predictive accuracy was assessed through mean absolute error and root mean square error. Results: When applying the algorithm, the predicted mean EQ-5D-3L index value was 0.77 compared to the observed mean index value of 0.75. Prediction error was higher for individuals reporting EQ-5D values <0.5 compared to individuals reporting EQ-5D values ≥0.5. Mean absolute error (0.12) and root mean square error (0.18) were smaller or equal to the prediction errors found in the original mapping study. Conclusion: The mapping algorithm had similar predictive accuracy in the two independent samples although results showed that the highest predictive performance was found in groups with better health. Varied predictive accuracy in subgroups is consistent with previous studies and strategies to deal with this are warranted.

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