Demonstration of a Population-Level Biomarker Testing Economic Evaluation Tool for Oncology Interventions
Author(s)
Lee J. Smolen, BS, Timothy M. Klein, BS, Pamela A. Martin, PhD, Olivia J. Schenck, BS, Hannah C. Smolen, Student, Harry J. Smolen, MS.
Medical Decision Modeling Inc., Indianapolis, IN, USA.
Medical Decision Modeling Inc., Indianapolis, IN, USA.
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