Mapping MOS sleep scale scores to SF-6D utility index
Mapping MOS sleep scale scores to SF-6D utility index
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DOI：
10.1185/030079907x210796
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发表时间：
2007-09-01
影响因子：
2.3
通讯作者：
Gajria, Kavita
中科院分区：
文献类型：
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作者：
Yang, Min;Dubois, Dominique;Gajria, Kavita
目标：得出医疗结果研究 (MOS) 睡眠量表的偏好评分将使其能够用于成本效用分析。本研究的目的是将 MOS 睡眠量表的分数映射到 SF-36 健康调查 (SF-36) 评分的基于偏好的健康状态效用指数 (SF-6D)。 研究设计和方法：使用了三个数据集：(1) MOS 研究，一项针对慢性病患者的为期 4 年的观察性研究，(2) 一项为期 7 周的开放标签、非比较性渗透控释口服给药系统 (OROS) 临床试验氢吗啡酮治疗慢性腰痛 (CLBP)，以及 (3) OROS 氢吗啡酮治疗慢性骨关节炎 (OA) 相关疼痛的为期 6 周的开放标签随机对照试验。测试了各种模型，其中 SF-6D 在 MOS 的 1000 个随机半（发育）样本（n = 1413）中回归到睡眠问题指数 II (SLP9)。最佳拟合模型应用于 MOS 的其他 1000 个随机半（交叉验证）样本（n = 1412）和两个试验样本（CLBP 试验中 n = 199；OA 试验中 n = 124）。结果：MOS 样本中的最佳拟合模型包括 SLP9 的二次项，它解释了发育样本中 SF-6D 的 34% 方差。 SLP9 分数较高时，预测错误最大。在模型中添加人口统计和临床变量解释了 SF-6D 评分中最小增量方差 1（< 5%）。这些结果在交叉验证 MOS 样本中得到了重复。在开发和交叉验证 MOS 样本中，平均预测和观察到的 SF-6D 分数几乎相同。当 MOS 中开发的映射算法应用于 CLBP 样本时，在基线和最终访问中，平均预测 SF-6D 分数比观察到的 SF-6D 分数高 0.09 分，而预测和观察到的 SF-6D 分数的变化是相同的。 结论：结果表明，可以将 MOS SLP9 映射到 SF-6D，产生对于成本效用分析至关重要的可用的基于偏好的分数。一个限制涉及对根据 SLP9 分数高于 60 估计的 SF-6D 分数的解释，其中预测误差显着增加。
Objective: Deriving preference scores for the Medical Outcomes Study (MOS) Sleep Scale would enable its use in cost-utility analyses. The objective of this study was to map scores of the MOS Sleep Scale to a preference-based healthstate utility index (SF-6D) scored from the SF-36 Health Survey (SF-36).Research design and methods: Three datasets were used: (1) the MOS study, a 4-year observational study of chronically ill patients, (2) a 7-week open-label, non-comparative clinical trial of an osmotic controlled- release oral delivery system (OROS) hydromorphone in the treatment of chronic low back pain (CLBP), and (3) a 6-week open-label randomized controlled trial of OROS hydromorphone in the treatment of pain associated with chronic osteoarthritis (OA). Various models were tested, where SF-6D was regressed onto the Sleep Problem Index-II (SLP9) in 1000 random half (developmental) samples of the MOS (n = 1413). The best fitting model was applied to the other 1000 random half (cross-validation) samples of the MOS (n = 1412), and to the two trial samples (n = 199 in the CLBP trial; n = 124 in the OA trial).Results: The best fitting model in the MOS samples included a quadratic term for the SLP9 which explained 34% of the variance in SF-6D in the developmental samples. Errors in prediction were greatest at higher SLP9 scores. Addition of demographic and clinical variables to the model explained minimal incremental amounts of variance 1(< 5%) in SF-6D scores. These results were replicated in the cross-validation MOS samples. In both developmental and cross-validation MOS samples, mean predicted and observed SF-6D scores were nearly identical. When the mapping algorithm developed in the MOS was applied to the CLBP sample, mean predicted SF-6D scores were 0.09 points higher than observed SF-6D scores at both baseline and final visits, while changes in predicted and observed SF-6D scores were identical.Conclusion: Results indicate that it is possible to map MOS SLP9 to SF-6D yielding useable preference-based scores essential for cost-utility analyses. A limitation concerns the interpretation of SF-6D scores estimated from SLP9 scores above 60, where the prediction errors increased considerably.