| Analytical approach |
1 |
The correct perspective to use remains uncertain |
Future studies should consider multiple analytical perspectives. As costs and effects are likely to be incurred over long time horizons, discount rates should be carefully selected. Analyses should be undertaken in an iterative manner and rerun when new evidence becomes available: this may require extensions in health economic modeling capacity. Study designs must take into account all potential comparators (both genomic and nongenomic) and all subsequent therapeutic decisions. In general, realistic simulations and comprehensive parameter and structural sensitivity analyses should be standard practice |
| 2 |
The appropriate analytical timeframe can be many years in length. Studies that focus on the short-term costs and consequences of genomic interventions risk misestimating cumulative costs |
| 3 |
The timing of an economic evaluation of a genomic test is critical as standard testing practice evolves continuously |
| 4 |
The choice of comparator and the specification of the study design can significantly impact on economic evaluation results |
| Costs and resource use |
1 |
A large number of cost categories are potentially relevant when conducting an economic evaluation in this context, including the costs of patient recruitment, sample collection and testing, data analysis, communication of test results, and actions taken based on test results |
A much broader range of costs must be included in genomic economic evaluations, with these costs collected at more frequent intervals. Analyses must also be increasingly flexible in order to account for temporal and geographical price variations. Costing studies of platform diagnostics with multiple applications should be conducted particularly carefully |
| 2 |
There are no national pricing tariffs for genomic tests. Costs vary considerably both within and between countries |
| 3 |
The combination of unstable tumor genomes and evolving test filters may require genomic tests to be conducted more frequently |
| Measuring outcomes |
1 |
Disease-specific and preference-based outcome measures limit comparability and do not capture all relevant dimensions of outcomes |
Studies that evaluate outcome measures in this context, which are not disease-specific or preference-based, would be useful. In particular, methods that can be used to value outcomes from genomic interventions within a cost–benefit analysis framework would be particularly valued. As individual outcomes may be more important than population outcomes, subgroup analysis is likely to be crucial |
| 2 |
Capturing information on personal utility may be important, but the metrics for measuring personal utility are not well established |
| 3 |
Cost–benefit analyses may have a greater role to play in this context than other forms of economic evaluation |
| 4 |
Individual outcomes may be more important than population outcomes |
| Measuring effectiveness |
1 |
Little is known about how patients and clinicians behave when faced with information provided by genomic interventions |
Behavioral uncertainty should be incorporated into analyses. The individualistic nature of treatment should also feed through to study design. There is a greater need for post-implementation economic analyses and evaluations, based on pragmatic trials. Finally, studies that consider the use of summary measures, such as polygenic risk scores, within economic evaluations are likely to be valued, as well as studies that better link genomic data with health outcome measures |
| 2 |
The quality of effectiveness data is weak |
| 3 |
Effectiveness data for genomic interventions, when available, are complex and challenging to incorporate into standard health economic analyses |
| Other |
1 |
Performance standards for genomic interventions vary considerably between laboratories |
Analyses should consider all possible models of service delivery |
| 2 |
The pace of innovation in genomics suggests that prioritizing investment in expensive yet informative comparative studies is desirable |
Value of information analysis is an important component of economic evaluations of genomic technologies |
