In many countries, economic evaluation is used as a practical tool for making decisions about the introduction and implementation of health technologies. Economic evaluation requires data on health outcomes and resource utilization associated with a technology to be combined in an analytical model to calculate the typical cost per life year or quality-adjusted life year (QALY) gained. PM is the next challenge for economic evaluation, and there is a growing number of published economic evaluations and reviews in this area.
This experience of performing economic evaluations of medical technologies in this area has generated a set of knowledge about how to develop new models incorporating the complex diagnostic criteria needed in PM, and about how to measure health outcomes and well-being measures related to the new information derived from genetic tests. There remain several unsolved questions, previously described by a number of authors, that pose challenges to the application of economic evaluation to PM [5,6,7,8]. In particular, the main elements that require further research and consensus include [5,6,7,8]:
The dependence of the results of economic evaluations in PM on the inclusion of healthcare costs and health outcomes derived from testing inaccuracies (i.e., false-negative, false-positive), which are subject to population variability and cannot easily be extrapolated from the results of clinical trials.
The lack of generalized and integrated observational ‘real-world’ databases for costs and health outcomes associated with PM technologies.
The necessity for fine-tuning data on costs of tests, with typically a number of tests, at varying costs, able to identify a particular genetic characteristic.
The failure of QALYs to fully capture the subtleties of PM health outcomes and related well-being.
The complexity needed in analytical decision models to incorporate the additional testing steps in the PM treatment algorithm (e.g., single vs. sequential tests), alongside all the other variables and parameters involved in any economic evaluation, resulting in a higher level of uncertainty around the final incremental cost-effectiveness ratio (ICER).
As a summary, we believe, together with other authors [7, 9, 10], that the lack of high-quality data on costs and health outcomes is the major reason why there is no clear evidence for the value of PM in terms of cost-effectiveness. We also note that even if more data were available, measuring the value of PM is inherently challenging, as there is still no commonly accepted definition of value .
Cost-effectiveness is based on the effectiveness achieved by the assessed technology. Usually, at the time of performing an initial economic evaluation, the available information on health outcomes comprises efficacy data from randomized controlled trials (RCTs) rather than real-world effectiveness. At this stage, the efficacy data used in evaluations of PM technologies are similar to those used in the assessment of other medicines. However, in the case of traditional medicines, once a product is in clinical use the data generated in clinical trials can be compared with the effectiveness results seen in real-world practice, validating (or challenging) the economic evaluation results already achieved. Notably, when real-world data in oncology have been collected and systematically compared with clinical trial evidence the results in practice have sometimes been aligned with the clinical trial setting, but on other occasions outcomes have deviated from expectations. Similarly, with the introduction of a new approach to medicine—such as PM—it is desirable to have at our disposal real-world studies showing the effectiveness of the new technologies. Compared with traditional medicine, implementation of PM requires additional tests and more complex protocols to achieve the anticipated health gains demonstrated in clinical trials. Additionally, clinical trial evidence for PM therapies may be less robust than for traditional medicines, as the evidence base may be only a subgroup of a larger trial, increasing the importance of gathering complementary data in clinical practice. If PM is to replace the traditional approach to medicine, successful incorporation of this additional complexity into real-world practice must be confirmed. As well as validating the results of economic evaluations, high-quality, real-world PM data would clarify the efficiency of new technologies. However, despite the need to improve the quality of economic evaluations in PM, to the best of our knowledge, the use of real-world data in this area is still limited.