What is the Impact of the Analysis Method Used for Health State Utility Values on QALYs in Oncology? A Simulation Study Comparing Progression-Based and Time-to-Death Approaches

Abstract

Background Health state utility values (‘utilities’) are an integral part of health technology assessment. Though traditionally categorised by disease status in oncology (i.e. progression), several recent assessments have adopted values calculated according to the time that measures were recorded before death. We conducted a simulation study to understand the limitations of each approach, with a focus on mismatches between the way utilities are generated, and analysed. Methods Survival times were simulated based on published literature, with permutations of three utility generation mechanisms (UGMs) and utility analysis methods (UAMs): (1) progression based, (2) time-to-death based, and (3) a ‘combination approach’. For each analysis quality-adjusted life-years (QALYs) were estimated. Goodness of fit was assessed via percentage mean error (%ME) and mean absolute error (%MAE). Scenario analyses were performed varying individual parameters, with complex scenarios mimicking published studies. The statistical code is provided for transparency and to aid future work in the area. Results %ME and %MAE were lowest when the correct analysis form was specified (i.e. UGM and UAM aligned). Underestimates were produced when a time-to-death element was present in the UGM but not included in the UAM, while the ‘combined’ UAM produced overestimates irrespective of the UGM. Scenario analysis demonstrated the importance of the volume of available data beyond the initial time period, for example follow-up. Conclusions We show that the use of an incorrectly or over-specified UAM can result in substantial bias in the estimation of utilities. We present a flowchart to highlight the issues that may be faced. Electronic supplementary material The online version of this article (10.1007/s40258-020-00620-6) contains supplementary material, which is available to authorized users.

Modelling Health State Utilities as a Transformation of Time to Death in Patients with Non-Small Cell Lung Cancer

“…Furthermore, recent retrospective analyses suggest that TTD may be more strongly correlated with health care costs than other factors, including age [ 5 , 6 ]. Finally, a simulation study has provided an indication of the situations for which TTD utilities will more closely reflect the deterioration in quality of life as patients approach death than the progression-based utilities approach [ 7 ], thus offering a better differentiator of the value of competing interventions.…”

“…In published implementations of TTD utilities, patient utility has been aggregated by discrete groupings of proximity to death, henceforth referred to as groupings. The selection of these groupings (e.g., 0–4 weeks, 4–26 weeks, 26–52 weeks and 52+ weeks from death) appears largely arbitrary, without a strong rationale (either clinical or empirical) for the specific groupings chosen [ 7 ].…”