Demonstration of a Population-Level Biomarker Testing Economic Evaluation Tool for Oncology Interventions
Moderator
Harry J. Smolen, MS
Speakers
Lee Smolen; Timothy Klein, BS; Pamela Martin, PhD; Olivia Schenck, Fishers, IN, United States; Hannah Smolen
OBJECTIVES: To demonstrate a population-level, biomarker testing, economic evaluation tool for oncology interventions with a hypothetical case study.
METHODS: The tool compares biomarker (BM) testing strategy impacts on costs and patient life-years (LYs). The BM prevalence (40%) of the patient cohort is specified. Two BM testing strategies (testing and no testing) are defined for comparison. A BM testing strategy implements a test with designated cost ($500), sensitivity (96%), and specificity (84%), and percentage of tested patients (100%). Targeted therapy cost per cycle is $6,000; chemotherapy, $3,000. Separate oncology interventions are specified for test-reported BM positive (BM+) and BM negative (BM-) patients. Clinical efficacies are based on median survival (reported in months) for BM true positive (BMT+) and BM true negative (BMT-) tumors: targeted therapy BMT+ overall survival (OS) 11.8, progression free survival (PFS) 4.9; targeted therapy BMT- OS 9.4, PFS 3.2; chemotherapy BMT+ OS 7.5, PFS 3.8; chemotherapy BMT-: OS 9.1, PFS 4.1. Results are reported for true positive and false positive populations. Scenario, one-way sensitivity, and probabilistic sensitivity analyses are used to explore the impact of varying inputs.
RESULTS: The BM testing strategy vs the BM no testing strategy resulted in a: 19.81% increase in undiscounted patient LYs (0.20 LYs per treated patient); 65.22% increase in overall (sum of treatment and BM testing) undiscounted costs ($16,340 per treated patient); and 63.22% increase in treatment costs ($15,840 per treated patient) attributable to use of the more expensive and more efficacious, i.e., longer treatment duration, targeted therapy for patients who tested BM+. Scenario results demonstrate the impact relatively small differences in testing sensitivity and specificity can have on patients receiving the correct treatment and resulting cost and effectiveness.
CONCLUSIONS: The tool provides a valuable and flexible tool for the analysis of population-level biomarker strategies in the current world of oncology treatment.
METHODS: The tool compares biomarker (BM) testing strategy impacts on costs and patient life-years (LYs). The BM prevalence (40%) of the patient cohort is specified. Two BM testing strategies (testing and no testing) are defined for comparison. A BM testing strategy implements a test with designated cost ($500), sensitivity (96%), and specificity (84%), and percentage of tested patients (100%). Targeted therapy cost per cycle is $6,000; chemotherapy, $3,000. Separate oncology interventions are specified for test-reported BM positive (BM+) and BM negative (BM-) patients. Clinical efficacies are based on median survival (reported in months) for BM true positive (BMT+) and BM true negative (BMT-) tumors: targeted therapy BMT+ overall survival (OS) 11.8, progression free survival (PFS) 4.9; targeted therapy BMT- OS 9.4, PFS 3.2; chemotherapy BMT+ OS 7.5, PFS 3.8; chemotherapy BMT-: OS 9.1, PFS 4.1. Results are reported for true positive and false positive populations. Scenario, one-way sensitivity, and probabilistic sensitivity analyses are used to explore the impact of varying inputs.
RESULTS: The BM testing strategy vs the BM no testing strategy resulted in a: 19.81% increase in undiscounted patient LYs (0.20 LYs per treated patient); 65.22% increase in overall (sum of treatment and BM testing) undiscounted costs ($16,340 per treated patient); and 63.22% increase in treatment costs ($15,840 per treated patient) attributable to use of the more expensive and more efficacious, i.e., longer treatment duration, targeted therapy for patients who tested BM+. Scenario results demonstrate the impact relatively small differences in testing sensitivity and specificity can have on patients receiving the correct treatment and resulting cost and effectiveness.
CONCLUSIONS: The tool provides a valuable and flexible tool for the analysis of population-level biomarker strategies in the current world of oncology treatment.
Conference/Value in Health Info
2025-05, ISPOR 2025, Montréal, Quebec, CA
Value in Health, Volume 28, Issue S1
Code
EE186
Topic
Economic Evaluation
Disease
SDC: Oncology