Sims, Andrew

Hall, Peter

Robertson, David

Tramonti, Giovanni

2023-04-17T17:32:08Z

2023-04-17T17:32:08Z

2023-04-17

BACKGROUND: The development of new biomarkers allows for the accurate prediction of breast cancer therapy response in the neoadjuvant setting. The implementation of these tools in routine NHS operations could have the potential for better clinical outcomes and a more cost-effective use of resources. AIMS: A decision-analytic modelling platform was created to evaluate the clinical effectiveness and cost-effectiveness of on-treatment biomarker-based predictive tools in a routine NHS implementation. The biomarker-based tool used in the simulation was EER4, developed at Edinburgh Institute of Genetics and Cancer. METHODS: Patient simulation models were constructed by integrating molecular biomarker data with clinical outcomes and healthcare cost data. The simulation benefits from the incorporation of NHS patient data and it features two complementary sub-models: a Discrete Event Simulation for the neoadjuvant setting and a Markov cohort model for the adjuvant setting. The results of the simulation compare the per-patient costs and health outcomes, expressed as Quality Adjusted Life Years (QALY), for each of the evaluated strategies. Moreover, this study includes a decision and budget impact analysis of OncotypeDX after its introduction in Edinburgh’s Breast Clinic. RESULTS: Treatment-decision strategies that utilize EER4 are likely to be cost-effective. Specifically, EER4 in conjunction with PREDICT shows lower costs and marginally superior QALYs, compared to PREDICT alone or OncotypeDX with PREDICT. At a threshold of 20,000£/QALY, EER4 with PREDICT has an 86% probability of being a cost-effective alternative to the current standard of care. EER4 in conjunction with neoadjuvant Letrozole increases breast-conserving surgery rates, displacing radical mastectomy by 16%. The Probabilistic One-way Sensitivity Analysis shows that results are robust to the uncertainty of EER4 per-unit costs and clinical performance. The decision and budget impact analysis of OncotypeDX indicates that while this technology might reduce the number of chemotherapies administered, the unit cost is greater than any savings produced by chemotherapy displacement. CONCLUSION: The early cost-effectiveness analysis shows that these biomarker-based technologies are likely to be cost-effective. However, further research is needed to assess the clinical effectiveness of EER4. The simulation platform developed in this study has the potential for further evaluation of decision-making tools for other subtypes of breast malignancies.

https://hdl.handle.net/1842/40483

http://dx.doi.org/10.7488/era/3249

en

The University of Edinburgh

Patient-Matched Treatment Biomarkers

Patient-Matched Pre-treatment Biomarkers

Patient-Matched On-Treatment Biomarkers

Cancer Therapy

Cancer Therapy Response Prediction

Cost-effectiveness

breast cancer therapy response

neoadjuvant setting

routine NHS operations

Quality Adjusted Life Years

Markov cohort model

Cost-effectiveness of patient-matched pre- and on-treatment biomarkers in cancer therapy response prediction

Thesis or Dissertation

Doctoral

PhD Doctor of Philosophy