Center for the Assessment of Pharmaceutical Practices (CAPP), Department of Health Law, Policy and Management, Boston University School of Public Health, Boston, MA, USA.
Outcomes Research Branch/Healthcare Delivery Research Program, Division of Cancer Control and Population Sciences, National Cancer Institute, Rockville, MD, USA.
Qual Life Res. 2018 Aug;27(8):2195-2206. doi: 10.1007/s11136-018-1850-3. Epub 2018 Apr 19.
To develop bridging algorithms to score the Veterans Rand-12 (VR-12) scales for comparability to those of the SF-36® for facilitating multi-cohort studies using data from the National Cancer Institute Surveillance, Epidemiology, and End Results Program (SEER) linked to Medicare Health Outcomes Survey (MHOS), and to provide a model for minimizing non-statistical error in pooled analyses stemming from changes to survey instruments over time.
Observational study of MHOS cohorts 1-12 (1998-2011). We modeled 2-year follow-up SF-36 scale scores from cohorts 1-6 based on baseline SF-36 scores, age, and gender, yielding 100 clusters using Classification and Regression Trees. Within each cluster, we averaged follow-up SF-36 scores. Using the same cluster specifications, expected follow-up SF-36 scores, based on cohorts 1-6, were computed for cohorts 7-8 (where the VR-12 was the follow-up survey). We created a new criterion validity measure, termed "extensibility," calculated from the square root of the mean square difference between expected SF-36 scale averages and observed VR-12 item score from cohorts 7-8, weighted by cluster size. VR-12 items were rescored to minimize this quantity.
Extensibility of rescored VR-12 items and scales was considerably improved from the "simple" scoring method for comparability to the SF-36 scales.
The algorithms are appropriate across a wide range of potential subsamples within the MHOS and provide robust application for future studies that span the SF-36 and VR-12 eras. It is possible that these surveys in a different setting outside the MHOS, especially in younger age groups, could produce somewhat different results.
开发桥接算法来对 Veterans Rand-12(VR-12)量表进行评分,以使其与 SF-36®量表具有可比性,从而方便使用国家癌症研究所监测、流行病学和结果计划(SEER)与医疗保险健康结果调查(MHOS)相关联的数据进行多队列研究,并为最小化由于调查工具随时间变化而导致的汇总分析中的非统计误差提供模型。
对 MHOS 队列 1-12(1998-2011)进行观察性研究。我们根据基线 SF-36 评分、年龄和性别,对队列 1-6 的 2 年随访 SF-36 量表评分进行建模,使用分类和回归树生成 100 个聚类。在每个聚类中,我们平均随访 SF-36 评分。使用相同的聚类规范,根据队列 1-6 计算队列 7-8(VR-12 是随访调查)的预期随访 SF-36 评分。我们创建了一个新的标准有效性度量,称为“可扩展性”,它是从预期 SF-36 量表平均值与队列 7-8 中观察到的 VR-12 项目得分的均方根差的平方根计算得出的,权重为聚类大小。VR-12 项目被重新评分以最小化该数量。
与 SF-36 量表相比,重新评分的 VR-12 项目和量表的可扩展性有了很大提高。
这些算法适用于 MHOS 内的广泛潜在子样本,为跨越 SF-36 和 VR-12 时代的未来研究提供了稳健的应用。在 MHOS 之外的不同环境中,特别是在年轻人群中,这些调查可能会产生略有不同的结果。