Department of Pharmaceutical Health Services Research, University of Maryland School of Pharmacy, Baltimore, MD.
NRG Oncology Statistics and Data Management Center, Philadelphia, PA.
JCO Clin Cancer Inform. 2022 Jun;6:e2100188. doi: 10.1200/CCI.21.00188.
To compare the predictive ability of mapping algorithms derived using cross-sectional and longitudinal data.
This methodological assessment used data from a randomized controlled noninferiority trial of patients with low-risk prostate cancer, conducted by NRG Oncology (ClinicalTrials.gov identifier: NCT00331773), which examined the efficacy of conventional schedule versus hypofractionated radiation therapy (three-dimensional conformal external beam radiation therapy/IMRT). Health-related quality-of-life data were collected using the Expanded Prostate Cancer Index Composite (EPIC), and health utilities were obtained using EuroQOL-5D-3L (EQ-5D) at baseline and 6, 12, 24, and 60 months postintervention. Mapping algorithms were estimated using ordinary least squares regression models through five-fold cross-validation in baseline cross-sectional data and combined longitudinal data from all assessment periods; random effects specifications were also estimated in longitudinal data. Predictive performance was compared using root mean square error. Longitudinal predictive ability of models obtained using baseline data was examined using mean absolute differences in the reported and predicted utilities.
A total of 267 (and 199) patients in the estimation sample had complete EQ-5D and EPIC domain (and subdomain) data at baseline and at all subsequent assessments. Ordinary least squares models using combined data showed better predictive ability (lowest root mean square error) in the validation phase for algorithms with EPIC domain/subdomain data alone, whereas models using baseline data outperformed other specifications in the validation phase when patient covariates were also modeled. The mean absolute differences were lower for models using EPIC subdomain data compared with EPIC domain data and generally decreased as the time of assessment increased.
Overall, mapping algorithms obtained using baseline cross-sectional data showed the best predictive performance. Furthermore, these models demonstrated satisfactory longitudinal predictive ability.
比较使用横断面和纵向数据得出的映射算法的预测能力。
本方法评估使用了 NRG Oncology 进行的低危前列腺癌患者随机对照非劣效性试验的数据（ClinicalTrials.gov 标识符：NCT00331773），该试验检验了常规方案与低分割放射治疗（三维适形外照射放射治疗/调强放射治疗）的疗效。使用扩展前列腺癌指数综合量表（EPIC）收集健康相关生活质量数据，使用欧洲五维健康量表 3 级简表（EQ-5D）在基线和干预后 6、12、24 和 60 个月时获得健康效用。通过五重交叉验证在基线横断面数据和所有评估期的组合纵向数据中使用普通最小二乘回归模型估计映射算法；也在纵向数据中估计了随机效应规范。通过均方根误差比较预测性能。使用报告和预测效用的平均绝对差异检查使用基线数据获得的模型的纵向预测能力。
在估计样本中，共有 267 名（和 199 名）患者在基线和所有后续评估中具有完整的 EQ-5D 和 EPIC 域（和子域）数据。在验证阶段，使用组合数据的普通最小二乘模型显示，对于仅使用 EPIC 域/子域数据的算法，具有更好的预测能力（最低均方根误差），而当还对患者协变量进行建模时，使用基线数据的模型在验证阶段表现优于其他规范。与 EPIC 域数据相比，使用 EPIC 子域数据的模型的平均绝对差异较低，并且随着评估时间的增加而通常降低。
总体而言，使用基线横断面数据得出的映射算法显示出最佳的预测性能。此外，这些模型表现出令人满意的纵向预测能力。
