An early economic evaluation to inform the translation into clinical practice of a spectroscopic liquid biopsy for the detection of brain cancer. Two specific aims are (1) to update an existing economic model with results from a prospective study of diagnostic accuracy and (2) to explore the potential of brain tumor-type predictions to affect patient outcomes and healthcare costs.

A cost-effectiveness analysis from a UK NHS perspective of the use of spectroscopic liquid biopsy in primary and secondary care settings, as well as a cost–consequence analysis of the addition of tumor-type predictions was conducted. Decision tree models were constructed to represent simplified diagnostic pathways. Test diagnostic accuracy parameters were based on a prospective validation study. Four price points (GBP 50-200, EUR 57-228) for the test were considered.

In both settings, the use of liquid biopsy produced QALY gains. In primary care, at test costs below GBP 100 (EUR 114), testing was cost saving. At GBP 100 (EUR 114) per test, the ICER was GBP 13,279 (EUR 15,145), whereas at GBP 200 (EUR 228), the ICER was GBP 78,300 (EUR 89,301). In secondary care, the ICER ranged from GBP 11,360 (EUR 12,956) to GBP 43,870 (EUR 50,034) across the range of test costs.

The results demonstrate the potential for the technology to be cost-effective in both primary and secondary care settings. Additional studies of test use in routine primary care practice are needed to resolve the remaining issues of uncertainty—prevalence in this patient population and referral behavior.

Data from randomized controlled trials (RCTs) are the primary source for health technology assessment (HTA) however these are limited by strict patient inclusion criteria, leading to concerns about whether treatment benefit estimates are accurate for all patients (generalizability). Real-World Data (RWD) have been proposed as a solution however as these are observational data there is additional potential for bias when estimating treatment effectiveness. To maximize the utility of RWD it is useful to consider the whole process of evidence generation and robustly address issues of feasibility and validity.

A series of complementary studies investigated whether population-based routinely collected health data from Scotland are suitable for estimating the effectiveness of chemotherapy for early breast cancer. Firstly, a prognostic score was validated in this population. Secondly, a comparison of RWD and randomized trial effectiveness estimates was made to investigate feasibility and validity of several methods – Propensity Score Matching (PSM), Instrumental variables (IV) and Regression Discontinuity. Finally, effectiveness estimates in trial underrepresented groups were produced.

PSM and IV were feasible and produced results in relatively close agreement with randomized data. Effectiveness estimates in trial underrepresented groups (women over 70 years and women with high comorbidity) were consistent with an approximate one-third reduction in the risk of death from breast cancer. This is equivalent to approximately a 3–4 percentage point difference in all cause mortality over 10 years in these groups.

RWD are a feasible for generating estimates of effectiveness of adjuvant chemotherapy in early stage breast cancer. The process of using RWD for this purpose should include careful assessment of data quality and comparison of alternative strategies for causal identification in the context of available randomized data.