Department of Engineering & Public Policy, Carnegie Mellon University, Pittsburgh, PA, USA.
Department of Health Policy and Management, Graduate School of Public Health, University of Pittsburgh, Pittsburgh, PA, USA.
Value Health. 2020 Mar;23(3):370-378. doi: 10.1016/j.jval.2019.09.2752. Epub 2019 Dec 30.
The Patient-Reported Outcomes Measurement Information System® (PROMIS) Profile instruments measure health status on 8 PROMIS domains. The PROMIS-Preference (PROPr) score provides a preference-based summary score for health states defined by 7 PROMIS domains. The Profile and PROPr share 6 domains; PROPr has 1 unique domain (Cognitive Function-Abilities), and the Profile has 2 unique domains (Anxiety and Pain Intensity). We produce an equation for calculating PROPr utility scores with Profile data.
We used data from 3982 members of US online survey panels who have scores on all 9 PROMIS domains. We used a 70%/30% split for model fit/validation. Using root-mean-square error and mean error on the utility scale, we compared models for predicting the missing Cognitive Function score via (A) the population average; (B) a score representing excellent cognitive function; (C) a score representing poor cognitive function; (D) a score predicted from linear regression of the 8 profile domains; and (E) a score predicted from a Bayesian neural network of the 8 profile domains.
The mean errors in the validation sample on the PROPr scale (which ranges from -0.022 to 1.00) for the models were: (A) 0.025, (B) 0.067, (C) -0.23, (D) 0.018, and (E) 0.018. The root-mean-square errors were: (A) 0.097, (B) 0.12, (C) 0.29, (D) 0.095, and (E) 0.094.
Although the Bayesian neural network had the best root-mean-square error for producing PROPr utility scores from Profile instruments, linear regression performs almost as well and is easier to use. We recommend the linear model for producing PROPr utility scores for PROMIS Profiles.
患者报告结局测量信息系统®（PROMIS）概况量表在 8 个 PROMIS 领域测量健康状况。PROMIS 偏好（PROPr）评分提供了由 7 个 PROMIS 领域定义的健康状态的偏好综合评分。概况和 PROPr 共有 6 个领域；PROPr 有 1 个独特的领域（认知功能-能力），而概况有 2 个独特的领域（焦虑和疼痛强度）。我们用概况数据生成了计算 PROPr 效用评分的方程。
我们使用了来自美国在线调查小组的 3982 名成员的数据，这些成员在所有 9 个 PROMIS 领域都有评分。我们使用 70%/30%的比例来划分模型拟合/验证。使用均方根误差和效用尺度上的平均误差，我们比较了通过以下方式预测缺失的认知功能评分的模型：（A）人口平均值；（B）代表优秀认知功能的评分；（C）代表较差认知功能的评分；（D）通过 8 个概况领域的线性回归预测的评分；以及（E）通过 8 个概况领域的贝叶斯神经网络预测的评分。
验证样本中 PROPr 量表上的平均误差（范围从-0.022 到 1.00）为：（A）0.025，（B）0.067，（C）-0.23，（D）0.018，和（E）0.018。均方根误差为：（A）0.097，（B）0.12，（C）0.29，（D）0.095，和（E）0.094。
虽然贝叶斯神经网络在从概况仪器生成 PROPr 效用评分方面具有最佳的均方根误差，但线性回归的表现几乎相同，且更容易使用。我们建议使用线性模型为 PROMIS 概况生成 PROPr 效用评分。
