Cook Karon F, Schalet Benjamin D, Kallen Michael A, Rutsohn Joshua P, Cella David
Department of Medical Social Sciences, Northwestern University Feinberg School of Medicine, 625 Michigan Ave, 27th Floor, Chicago, IL, USA.
Qual Life Res. 2015 Oct;24(10):2305-18. doi: 10.1007/s11136-015-0987-6. Epub 2015 Apr 18.
The study purposes were to mathematically link scores of the Brief Pain Inventory Pain Interference Subscale and the Short Form-36 Bodily Pain Subscale (legacy pain interference measures) to the NIH Patient-Reported Outcome Measurement Information System (PROMIS(®)) Pain Interference (PROMIS-PI) metric and evaluate results.
Linking was accomplished using both equipercentile and item response theory (IRT) methods. Item parameters for legacy items were estimated on the PROMIS-PI metric to allow for pattern scoring. Crosswalk tables also were developed that associated raw scores (summed or average) on legacy measures to PROMIS-PI scores. For each linking strategy, participants' actual PROMIS-PI scores were compared to those predicted based on their legacy scores. To assess the impact of different sample sizes, we conducted random resampling with replacement across 10,000 replications with sample sizes of n = 25, 50, and 75.
Analyses supported the assumption that all three scales were measuring similar constructs. IRT methods produced marginally better results than equipercentile linking. Accuracy of the links was substantially affected by sample size.
The linking tools (crosswalks and item parameter estimates) developed in this study are robust methods for estimating the PROMIS-PI scores of samples based on legacy measures. We recommend using pattern scoring for users who have the necessary software and score crosswalks for those who do not.
本研究旨在将简明疼痛问卷疼痛干扰分量表和简短健康调查问卷-36身体疼痛分量表（传统疼痛干扰测量指标）的得分与美国国立卫生研究院患者报告结局测量信息系统（PROMIS(®)）疼痛干扰（PROMIS-PI）指标进行数学关联，并评估结果。
使用等百分位法和项目反应理论（IRT）方法进行关联。在PROMIS-PI指标上估计传统项目的项目参数，以便进行模式评分。还制定了对照表，将传统测量指标的原始得分（总和或平均值）与PROMIS-PI得分相关联。对于每种关联策略，将参与者的实际PROMIS-PI得分与其基于传统得分预测的得分进行比较。为了评估不同样本量的影响，我们进行了有放回的随机重抽样，重复10000次，样本量分别为n = 25、50和75。
分析支持了所有三个量表都在测量相似结构的假设。IRT方法产生的结果略优于等百分位关联。关联的准确性受样本量的显著影响。
本研究中开发的关联工具（对照表和项目参数估计）是基于传统测量指标估计样本PROMIS-PI得分的可靠方法。对于有必要软件的用户我们建议使用模式评分，对于没有的用户建议使用得分对照表。
