TY - JOUR
T1 - Cost-effectiveness of the Decipher Genomic Classifier to Guide Individualized Decisions for Early Radiation Therapy After Prostatectomy for Prostate Cancer
AU - Lobo, Jennifer M.
AU - Trifiletti, Daniel M.
AU - Sturz, Vanessa N.
AU - Dicker, Adam P.
AU - Buerki, Christine
AU - Davicioni, Elai
AU - Cooperberg, Matthew R.
AU - Karnes, R. Jeffrey
AU - Jenkins, Robert B.
AU - Den, Robert B.
AU - Showalter, Timothy N.
N1 - Publisher Copyright:
© 2016 Elsevier Inc.
PY - 2017/6
Y1 - 2017/6
N2 - It is not currently clear which patients will benefit from adjuvant radiation therapy after prostatectomy. A genomic classifier assay to estimate the individualized risk of prostate cancer progression would help physicians offer personalized decision-making for adjuvant therapy after prostatectomy. We present the results of a cost-effectiveness analysis that applies an individualized decision analysis framework to estimate the costs and outcomes for adjuvant therapy decisions after radical prostatectomy using a genomic classifier test (Decipher). Genomic classifier-based treatment decision-making was shown to be a cost-effective alternative compared with usual care and 100% usage of adjuvant therapy. Background Controversy exists regarding the effectiveness of early adjuvant versus salvage radiation therapy after prostatectomy for prostate cancer. Estimates of prostate cancer progression from the Decipher genomic classifier (GC) could guide informed decision-making and improve the outcomes for patients. Materials and Methods We developed a Markov model to compare the costs and quality-adjusted life years (QALYs) associated with GC-based treatment decisions regarding adjuvant therapy after prostatectomy with those of 2 control strategies: usual care (determined from patterns of care studies) and the alternative of 100% adjuvant radiation therapy. Using the bootstrapping method of sampling with replacement, the cases of 10,000 patients were simulated during a 10-year time horizon, with each subject having individual estimates for cancer progression (according to GC findings) and noncancer mortality (according to age). Results GC-based care was more effective and less costly than 100% adjuvant radiation therapy and resulted in cost savings up to an assay cost of $11,402. Compared with usual care, GC-based care resulted in more QALYs. Assuming a $4000 assay cost, the incremental cost-effectiveness ratio was $90,833 per QALY, assuming a 7% usage rate of adjuvant radiation therapy. GC-based care was also associated with a 16% reduction in the percentage of patients with distant metastasis at 5 years compared with usual care. Conclusion The Decipher GC could be a cost-effective approach for genomics-driven cancer treatment decisions after prostatectomy, with improvements in estimated clinical outcomes compared with usual care. The individualized decision analytic framework applied in the present study offers a flexible approach to estimate the potential utility of genomic assays for personalized cancer medicine.
AB - It is not currently clear which patients will benefit from adjuvant radiation therapy after prostatectomy. A genomic classifier assay to estimate the individualized risk of prostate cancer progression would help physicians offer personalized decision-making for adjuvant therapy after prostatectomy. We present the results of a cost-effectiveness analysis that applies an individualized decision analysis framework to estimate the costs and outcomes for adjuvant therapy decisions after radical prostatectomy using a genomic classifier test (Decipher). Genomic classifier-based treatment decision-making was shown to be a cost-effective alternative compared with usual care and 100% usage of adjuvant therapy. Background Controversy exists regarding the effectiveness of early adjuvant versus salvage radiation therapy after prostatectomy for prostate cancer. Estimates of prostate cancer progression from the Decipher genomic classifier (GC) could guide informed decision-making and improve the outcomes for patients. Materials and Methods We developed a Markov model to compare the costs and quality-adjusted life years (QALYs) associated with GC-based treatment decisions regarding adjuvant therapy after prostatectomy with those of 2 control strategies: usual care (determined from patterns of care studies) and the alternative of 100% adjuvant radiation therapy. Using the bootstrapping method of sampling with replacement, the cases of 10,000 patients were simulated during a 10-year time horizon, with each subject having individual estimates for cancer progression (according to GC findings) and noncancer mortality (according to age). Results GC-based care was more effective and less costly than 100% adjuvant radiation therapy and resulted in cost savings up to an assay cost of $11,402. Compared with usual care, GC-based care resulted in more QALYs. Assuming a $4000 assay cost, the incremental cost-effectiveness ratio was $90,833 per QALY, assuming a 7% usage rate of adjuvant radiation therapy. GC-based care was also associated with a 16% reduction in the percentage of patients with distant metastasis at 5 years compared with usual care. Conclusion The Decipher GC could be a cost-effective approach for genomics-driven cancer treatment decisions after prostatectomy, with improvements in estimated clinical outcomes compared with usual care. The individualized decision analytic framework applied in the present study offers a flexible approach to estimate the potential utility of genomic assays for personalized cancer medicine.
KW - Adjuvant and salvage radiation therapy
KW - Cost-effectiveness analysis
KW - Individualized medicine
KW - Molecular assay
KW - Prostatic neoplasms
UR - http://www.scopus.com/inward/record.url?scp=85009468133&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85009468133&partnerID=8YFLogxK
U2 - 10.1016/j.clgc.2016.08.012
DO - 10.1016/j.clgc.2016.08.012
M3 - Article
C2 - 28089723
AN - SCOPUS:85009468133
SN - 1558-7673
VL - 15
SP - e299-e309
JO - Clinical Genitourinary Cancer
JF - Clinical Genitourinary Cancer
IS - 3
ER -