Understanding the upper bound of survival for conducting and critically appraising economic evaluations of low-risk cancers

In most developed countries, overall survival rates for low-riskcancers (e.g. localized prostate and thyroid cancer) are comparable tothose of the general population. The general population’s survival rate mayserve as an upper bound for survival in people with these cancers. By applying this concept, we demonstrated limitations of using Markovmodels for low-risk cancers, and proposed an alternative modellingapproach.  Markov disease progression models typically depict a gradual progressionfrom early to advanced cancer stages and an increasing risk of cancer-specificmortality over time. However, data showed that the risk of death from cancer wasoften the greatest within the first few years following diagnosis. We thereforeproposed an alternative modeling approach. This method involves calculatingthe average quality-adjusted life-years (QALYs) from the integrals of thesurvival curve, multiplied by the corresponding health utility curve.Alternatively, QALYs may be estimated by averaging survival and utility withineach time interval and summing these estimates across intervals. We also appliedthese concepts to the critical appraisal of published economic evaluations. Understanding the upper bound of survival for low-risk cancers enableshealth economists to more accurately conduct cost-effectiveness analyses andassess the credibility of published economic evaluations.