Introduction
Cancer
CANCER
Since the results of a cost-effectiveness analysis help to properly allocate limited medical resources, many countries utilize it for decision-making regarding healthcare systems [The EQ-5D is frequently employed with disease-specific health-related quality of life (HRQOL) instruments in clinical studies, such as randomized controlled trials that often provide health utility information for health economic evaluation. The EQ-5D and the disease-specific HRQOL instruments are used simultaneously; this is because the former’s five items incompletely describe patients’ health status for clinical assessment due to the smallness of the number of items, while the latter can detect disease-specific symptoms and treatment impacts with high sensitivity. Additionally, the two are utilized concurrently in research developing mapping algorithms from the disease-specific HRQOL instruments onto the EQ-5D indexes [However, theory of order effects and previous studies evaluating the order effects on the responses of the HRQOL instruments indicate the EQ-5D’s possible susceptibility to it. In this paper, order effects refer to the phenomenon that the different orders in which instruments or questions presented influence the responses to them. On the one hand, the order effects on HRQOL instruments consisting of many questions has been unobserved in three studies [If instrument orders impact the EQ-5D-5L responses, the order effects could bias the cost-effectiveness analysis results. For example, when health state utilities are extracted from multiple studies in which different orders of the disease-specific HRQOL instruments and the EQ-5D-5L are adopted, the aforementioned analysis’ results may be influenced due to the order effects. In mapping research, the latter may or may not be preceded by disease-specific measures that may also bias mapping algorithms, resulting in biased results of the cost-effectiveness analysis.This research investigated the impact of the preceding disease-specific HRQOL instruments on responses to the EQ-5D-5L in the field of oncology. Specifically, we focused on two common cancer-specific HRQOL instruments, namely, the European Organization for Research and Treatment of Cancer Quality of Life Questionnaire Core 30 (EORTC QLQ-C30) and the Functional Assessment of Cancer Therapy General (FACT-G) and evaluated the differences in the responses to the EQ-5D-5L’s five questions, its mean index, its correlations with the cancer-specific HRQOL instruments’ subscales, and the missingness of its index among the instrument orders.