Khan Kamran A, Madan Jason, Petrou Stavros, Lamb Sarah E
Warwick Clinical Trials Unit, Division of Health Sciences, Warwick Medical School, University of Warwick, Coventry, UK.
Warwick Clinical Trials Unit, Division of Health Sciences, Warwick Medical School, University of Warwick, Coventry, UK.
Value Health. 2014 Sep;17(6):686-95. doi: 10.1016/j.jval.2014.07.001.
The Roland Morris Questionnaire (RMQ) is a widely used health status measure for low back pain (LBP). It is not preference-based, and there are currently no established algorithms for mapping between the RMQ and preference-based health-related quality-of-life measures. Using data from randomized controlled trials of treatment for LBP, we sought to develop algorithms for mapping between RMQ scores and health utilities derived using either the EuroQol five-dimensional questionnaire (EQ-5D) or the six-dimensional health state short form (derived from Medical Outcomes Study 36-Item Short-Form Health Survey) (SF-6D).
This study is based on data from the Back Skills Training Trial in which data were collected from 701 patients at baseline and subsequently at 3, 6, and 12 months postrandomization using a range of outcome measures, including the RMQ, the EQ-5D, and the Short Form 12 item Health Survey (SF-12) (from which SF-6D utilities can be derived). We used baseline trial data to estimate models using both direct and response mapping approaches to predict EQ-5D and SF-6D health utilities and dimension responses. A multistage model selection process was used to assess the predictive accuracy of the models. We then explored different techniques and mapping models that made use of repeated follow-up observations in the data. The estimated mapping algorithms were validated using external data from the UK Back Pain Exercise and Manipulation trial.
A number of models were developed that accurately predict health utilities in this context. The best performing model for RMQ to EQ-5D mapping was a beta regression with Bayesian quasi-likelihood estimation that included 24 dummy variables for RMQ responses, age, and sex as covariates (mean squared error 0.0380) based on repeated data. The model selected for RMQ to SF-6D mapping was a finite mixture model that included the overall RMQ score, age, sex, RMQ score squared, age squared, and an interaction term for age and RMQ score as covariates (mean squared error 0.0114) based on repeated data.
It is possible to reasonably predict EQ-5D and SF-6D health utilities from RMQ scores and responses using regression methods. Our regression equations provide an empirical basis for estimating health utilities when EQ-5D or SF-6D data are not available. They can be used to inform future economic evaluations of interventions targeting LBP.
罗兰·莫里斯问卷（RMQ）是一种广泛用于评估腰痛（LBP）健康状况的工具。它并非基于偏好，目前也没有既定的算法可用于在RMQ与基于偏好的健康相关生活质量测量工具之间进行转换。利用腰痛治疗随机对照试验的数据，我们试图开发算法，以实现RMQ评分与使用欧洲五维健康量表（EQ - 5D）或六维健康状态简表（源自医学结局研究36项简短健康调查）（SF - 6D）得出的健康效用值之间的转换。
本研究基于背部技能训练试验的数据，该试验从701名患者基线时开始收集数据，随后在随机分组后的3个月、6个月和12个月使用一系列结局测量工具进行数据收集，包括RMQ、EQ - 5D和12项简短健康调查（SF - 12）（可从中得出SF - 6D效用值）。我们使用试验基线数据，通过直接和响应映射方法估计模型，以预测EQ - 5D和SF - 6D健康效用值及维度响应。采用多阶段模型选择过程评估模型的预测准确性。然后，我们探索了利用数据中重复随访观察结果的不同技术和映射模型。使用来自英国腰痛运动与手法治疗试验的外部数据对估计的映射算法进行验证。
在此背景下开发了多个能准确预测健康效用值的模型。对于RMQ到EQ - 5D映射，表现最佳的模型是采用贝叶斯拟似然估计的贝塔回归模型，该模型将RMQ响应的24个虚拟变量、年龄和性别作为协变量（基于重复数据的均方误差为0.0380）。为RMQ到SF - 6D映射选择的模型是一个有限混合模型，该模型将RMQ总分、年龄、性别、RMQ评分的平方、年龄的平方以及年龄与RMQ评分的交互项作为协变量（基于重复数据的均方误差为0.0114）。
使用回归方法可以根据RMQ评分和响应合理预测EQ - 5D和SF - 6D健康效用值。我们的回归方程为在没有EQ - 5D或SF - 6D数据时估计健康效用值提供了实证依据。它们可用于为未来针对腰痛的干预措施的经济评估提供参考。
