In:
PLOS ONE, Public Library of Science (PLoS), Vol. 18, No. 10 ( 2023-10-5), p. e0292239-
Abstract:
The objective of this study was to systematically analyse methodological and structural assumptions utilised in model-based health economic evaluations of systemic advanced hepatocellular carcinoma (HCC) therapies, discuss the existing challenges, and develop methodological recommendations for future models in advanced HCC. Methods We performed literature searches using five databases (Embase, PubMed, Web of Science, Econlit, and CNKI) up to December 4, 2022. Technology appraisals from Canada, England, Australia, and the United States were also considered. Model-based full economic evaluations of systemic advanced HCC therapies in English or Chinese met the eligibility criteria. The reporting quality was assessed by using the Consolidated Health Economic Evaluation Reporting Standards 2022 checklist. Results Of 12,863 records retrieved, 55 were eligible for inclusion. Markov model (n = 29, 53%) and partitioned survival model (n = 27, 49%) were the most commonly used modelling techniques. Most studies were based on health-state-driven structure (n = 51, 93%), followed by treatment-line-driven structure (n = 2, 4%) and combination structure (n = 1, 2%). Only three studies (5%) adopted external real-world data to extrapolate the overall survival or calibrate the extrapolation. Few studies reported the assumptions of transition probabilities. Utility modelling approaches were state-based (n = 51, 93%) and time-to-death (n = 1, 2%). Only 13 studies (24%) reported five types of model validation. Economic evaluation results of specific treatment strategies varied among studies. Conclusions Disease modelling for health economic evaluations of systemic therapies in advanced HCC has adopted various modelling approaches and assumptions, leading to marked uncertainties in results. By proposing methodological recommendations, we suggest that future model-based studies for health economic evaluation of HCC therapies should follow good modelling practice guidelines and improve modelling methods to generate reliable health and economic evidence.
Type of Medium:
Online Resource
ISSN:
1932-6203
DOI:
10.1371/journal.pone.0292239
DOI:
10.1371/journal.pone.0292239.g001
DOI:
10.1371/journal.pone.0292239.g002
DOI:
10.1371/journal.pone.0292239.g003
DOI:
10.1371/journal.pone.0292239.g004
DOI:
10.1371/journal.pone.0292239.g005
DOI:
10.1371/journal.pone.0292239.t001
DOI:
10.1371/journal.pone.0292239.t002
DOI:
10.1371/journal.pone.0292239.t003
DOI:
10.1371/journal.pone.0292239.t004
DOI:
10.1371/journal.pone.0292239.t005
DOI:
10.1371/journal.pone.0292239.t006
DOI:
10.1371/journal.pone.0292239.s001
DOI:
10.1371/journal.pone.0292239.s002
Language:
English
Publisher:
Public Library of Science (PLoS)
Publication Date:
2023
detail.hit.zdb_id:
2267670-3
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