COST-EFFECTIVENESS ANALYSIS OF AZACITIDINE VS. DECITABINE IN THE TREATMENT OF MYLODYSPLASTIC SYNDROMES IN CHINA

Author(s)

Zhao M, Chen B, Wu R, Fan CS
Beijing Medical and Health Economic Research Association, Beijing, China

OBJECTIVES: To assess the cost-effectiveness of azacitidine (AZA) relative to decitabine (DEC) in Chinese patients with the mylodysplastic syndromes (MDS) over a lifetime horizon from the perspective of health care payers.

METHODS: A lifetime cohort Markov model was constructed to evaluate the cost-effectiveness of AZA compared with DEC with the 4 health states including MDS with transfusion dependent (MDS+TD), MDS with transfusion independent (MDS+TI), acute myeloid leukemia (AML) and death in 4-week cycles until the death of 95% patients. Model parameters were derived from literature review and expert survey. Cost (2019 ¥/CNY) and outcomes were discounted at 5% annually. One-way sensitivity analysis and probabilistic sensitivity analysis based on 10,000 Monte Carlo simulations were performed.

RESULTS: In the base-case model, the patients treated with AZA were associated with better survival (life years: 3.125 vs. 2.536) and more quality-adjusted life years (QALYs) (2.049 vs. 1.621) compared with DEC. And AZA could save total direct medical costs for ¥66,829 compared with DEC. AZA was more effective and less costly compared with DEC. Sensitivity analysis demonstrated robustness of baseline results, with drug acquisition costs being the largest influence factor. The probability that AZA was cost effective comparing with DEC was 82.80% within the threshold of 3 times Chinese GDP per capita/QALYs.

CONCLUSIONS: The analysis indicated that the use of AZA in the treatment of MDS is a cost-effective option compared with DEC in the Chinese setting.

Conference/Value in Health Info

2020-05, ISPOR 2020, Orlando, FL, USA

Value in Health, Volume 23, Issue 5, S1 (May 2020)

Code

PCN131

Topic

Economic Evaluation

Topic Subcategory

Cost-comparison, Effectiveness, Utility, Benefit Analysis

Disease

Oncology

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