Mixture and Non-mixture Cure Models for Health Technology Assessment: What You Need to Know

Abstract

There is increasing interest in the use of cure modelling to inform health technology assessment (HTA) due to the development of new treatments that appear to offer the potential for cure in some patients. However, cure models are often not included in evidence dossiers submitted to HTA agencies, and they are relatively rarely relied upon to inform decision-making. This is likely due to a lack of understanding of how cure models work, what they assume, and how reliable they are. In this tutorial we explain why and when cure models may be useful for HTA, describe the key characteristics of mixture and non-mixture cure models, and demonstrate their use in a range of scenarios, providing Stata code. We highlight key issues that must be taken into account by analysts when fitting these models and by reviewers and decision-makers when interpreting their predictions. In particular, we note that flexible parametric non-mixture cure models have not been used in HTA, but they offer advantages that make them well suited to an HTA context when a cure assumption is valid but follow-up is limited.

Fig. 1

Kaplan–Meier survival plots

Fig. 2

Scenario 1 (medium cure fraction) survival and hazard plots. 5y bk 5-year boundary knot, 15y bk 15-year boundary knot

Fig. 3

Scenario 2 (low cure fraction) survival and hazard plots. 5y bk 5-year boundary knot, 15y bk 15-year boundary knot

Fig. 4

Scenario 3 (no cure) survival and hazard plots. 5y bk 5-year boundary knot, 15y bk 15-year boundary knot