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

BACKGROUND

In most developed countries, overall survival rates for low-risk cancers (e.g. localized prostate and thyroid cancer) are comparable to those of the general population. The general population's survival rate may serve as an upper bound for survival in people with these cancers.

METHODS

By applying this concept, we demonstrated limitations of using Markov models for low-risk cancers and proposed an alternative modeling approach.

RESULTS

Markov disease progression models typically depict a gradual progression from early to advanced cancer stages and an increasing risk of cancer-specific mortality over time. However, data showed that the risk of death from cancer was often the greatest within the first few years following diagnosis. We therefore proposed an alternative modeling approach. This method involves calculating the average quality-adjusted life-years (QALYs) from the integrals of the survival curve, multiplied by the corresponding health utility curve. Alternatively, QALYs may be estimated by averaging survival and utility within each time interval and summing these estimates across intervals. We also applied these concepts to the critical appraisal of published economic evaluations.

CONCLUSIONS

Understanding the upper bound of survival for low-risk cancers enables health economists to more accurately conduct cost-effectiveness analyses and assess the credibility of published economic evaluations. [Figure: see text].