Kiadaliri Aliasghar A, Englund Martin
Department of Clinical Sciences Lund, Orthopaedics, Clinical Epidemiology Unit, Lund University, Lund, Sweden.
Health Services Management Research Center, Institute for Futures Studies in Health, Kerman University of Medical Sciences, Kerman, Iran.
Health Qual Life Outcomes. 2016 Oct 4;14(1):141. doi: 10.1186/s12955-016-0547-y.
The use of mapping algorithms have been suggested as a solution to predict health utilities when no preference-based measure is included in the study. However, validity and predictive performance of these algorithms are highly variable and hence assessing the accuracy and validity of algorithms before use them in a new setting is of importance. The aim of the current study was to assess the predictive accuracy of three mapping algorithms to estimate the EQ-5D-3L from the Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC) among Swedish people with knee disorders. Two of these algorithms developed using ordinary least squares (OLS) models and one developed using mixture model.
The data from 1078 subjects mean (SD) age 69.4 (7.2) years with frequent knee pain and/or knee osteoarthritis from the Malmö Osteoarthritis study in Sweden were used. The algorithms' performance was assessed using mean error, mean absolute error, and root mean squared error. Two types of prediction were estimated for mixture model: weighted average (WA), and conditional on estimated component (CEC).
The overall mean was overpredicted by an OLS model and underpredicted by two other algorithms (P < 0.001). All predictions but the CEC predictions of mixture model had a narrower range than the observed scores (22 to 90 %). All algorithms suffered from overprediction for severe health states and underprediction for mild health states with lesser extent for mixture model. While the mixture model outperformed OLS models at the extremes of the EQ-5D-3D distribution, it underperformed around the center of the distribution.
While algorithm based on mixture model reflected the distribution of EQ-5D-3L data more accurately compared with OLS models, all algorithms suffered from systematic bias. This calls for caution in applying these mapping algorithms in a new setting particularly in samples with milder knee problems than original sample. Assessing the impact of the choice of these algorithms on cost-effectiveness studies through sensitivity analysis is recommended.
当研究中未纳入基于偏好的测量方法时，有人建议使用映射算法来预测健康效用。然而，这些算法的有效性和预测性能差异很大，因此在新环境中使用之前评估算法的准确性和有效性很重要。本研究的目的是评估三种映射算法在瑞典膝关节疾病患者中从西安大略和麦克马斯特大学骨关节炎指数（WOMAC）估计EQ-5D-3L的预测准确性。其中两种算法是使用普通最小二乘法（OLS）模型开发的，一种是使用混合模型开发的。
使用来自瑞典马尔默骨关节炎研究的1078名平均（标准差）年龄为69.4（7.2）岁、经常膝关节疼痛和/或膝关节骨关节炎患者的数据。使用平均误差、平均绝对误差和均方根误差评估算法的性能。对混合模型估计了两种类型的预测：加权平均（WA）和基于估计成分的条件预测（CEC）。
一个OLS模型高估了总体平均值，另外两种算法低估了总体平均值（P < 0.001）。除混合模型的CEC预测外，所有预测的范围都比观察分数窄（22%至90%）。所有算法对严重健康状态存在高估，对轻度健康状态存在低估，混合模型的低估程度较小。虽然混合模型在EQ-5D-3D分布极端情况下的表现优于OLS模型，但在分布中心附近表现较差。
虽然基于混合模型的算法与OLS模型相比更准确地反映了EQ-5D-3L数据的分布，但所有算法都存在系统偏差。这就要求在新环境中应用这些映射算法时要谨慎，特别是在膝关节问题比原始样本更轻的样本中。建议通过敏感性分析评估这些算法的选择对成本效益研究的影响。
