Pharmaceutical Outcomes & Policy (POP), College of Pharmacy, HPNP 3338, University of Florida, 1225 Center Drive, Gainesville, FL, 32610, USA.
Department of Pharmaceutical Outcomes and Policy College of Pharmacy, HPNP 2309, University of Florida, 1225 Center Drive Gainesville, FL, 32610, USA.
Res Social Adm Pharm. 2020 Aug;16(8):1095-1099. doi: 10.1016/j.sapharm.2019.11.009. Epub 2019 Nov 18.
Time series models are widely used forecasting techniques in health care for long time series and are typically built in commercial statistical packages. However, for short time series data, such as health-related quality of life (HRQoL), guidance on how to select and use appropriate time series models is lacking. This tutorial provides a step-by-step guide adopting a time series analysis framework for HRQoL forecasting.
We walk through a case study examining the forecasting of the effects of adjuvant endocrine therapy on the HRQoL of post-menopausal women with non-metastatic ER + breast cancer using data from the HRQoL sub-protocol of the Tamoxifen arm of the Arimidex, tamoxifen, alone or in combination (ATAC) trial.
The forecasting of HRQoL consists of four steps: 1) data extraction and accuracy check, 2) forecasting horizon definition and identification of data pattern, 3) forecasting model identification and fitting using five forecasting approaches appropriate for short time series ((i) double exponential smoothing, (ii) double moving average, (iii) fuzzy forecasting, (iv) grey forecasting, and (v) Volterra series), 4) forecasting model selection. A user-friendly visual basic for applications (VBA) Excel add-in is made available to interested users to facilitate the application of the tutorial.
The Grey method and Volterra series appeared to be good candidates to forecast the effects of adjuvant endocrine therapy on the HRQoL of post-menopausal women with non-metastatic ER + breast cancer enrolled in the ATAC trial.
It is feasible to forecast the effects of treatments on HRQOL even when the time series is short.
时间序列模型是医疗保健领域中广泛用于长期时间序列预测的技术，通常在商业统计软件包中构建。然而，对于短期时间序列数据，例如健康相关生活质量（HRQoL），缺乏关于如何选择和使用适当的时间序列模型的指导。本教程提供了一个采用时间序列分析框架进行 HRQoL 预测的分步指南。
我们通过一个案例研究，使用来自 ATAC 试验中他莫昔芬手臂的 HRQoL 子协议中的数据，探讨辅助内分泌治疗对非转移性 ER+乳腺癌绝经后妇女 HRQoL 的影响预测。
HRQoL 的预测包括四个步骤：1）数据提取和准确性检查，2）预测范围定义和数据模式识别，3）使用适用于短期时间序列的五种预测方法（i）双指数平滑，（ii）双移动平均，（iii）模糊预测，（iv）灰色预测和（v）Volterra 系列）识别和拟合预测模型，4）预测模型选择。为有兴趣的用户提供了一个用户友好的 Visual Basic for Applications（VBA）Excel 加载项，以方便本教程的应用。
灰色方法和 Volterra 系列似乎是预测辅助内分泌治疗对 ATAC 试验中入组的非转移性 ER+乳腺癌绝经后妇女 HRQoL 影响的良好候选方法。
即使时间序列较短，也可以预测治疗对 HRQOL 的影响。
