OBJECTIVES : 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. In this tutorial, 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 a clinical trial (Tamoxifen arm of the Arimidex, tamoxifen, alone or in combination (ATAC) trial) to improve clinical decision-making.

METHODS : 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. This tutorial provides a step-by-step guide adopting a time series analysis framework for HRQoL forecasting. A user-friendly visual basic for applications (VBA) Excel add-in is made available to interested users to facilitate the implementation of the short time series framework.

RESULTS : The Grey method and Volterra series appeared to be good candidates to forecast the HRQoL data.

CONCLUSIONS : Our Excel add-in represents an attractive forecasting tool alternative worth considering, specifically for short-term time series as it is usually the case for HRQoL.