Abstract
PurposeProton beam therapy (PBT) is associated with less toxicity relative to conventional photon radiotherapy for head-and-neck cancer (HNC). Upfront delivery costs are greater, but PBT can provide superior long-term value by minimizing treatment-related complications. Cost-effectiveness models (CEMs) estimate the relative value of novel technologies (such as PBT) as compared with the established standard of care. However, the uncertainties of CEMs can limit interpretation and applicability. This review serves to (1) assess the methodology and quality of pertinent CEMs in the existing literature, (2) evaluate their suitability for guiding clinical and economic strategies, and (3) discuss areas for improvement among future analyses.Materials and MethodsPubMed was queried for CEMs specific to PBT for HNC. General characteristics, modeling information, and methodological approaches were extracted for each identified study. Reporting quality was assessed via the Consolidated Health Economic Evaluation Reporting Standards 24-item checklist, whereas methodologic quality was evaluated via the Philips checklist. The Cooper evidence hierarchy scale was employed to analyze parameter inputs referenced within each model.ResultsAt the time of study, only 4 formal CEMs specific to PBT for HNC had been published (2005, 2013, 2018, 2020). The parameter inputs among these various Markov cohort models generally referenced older literature, excluding many clinically relevant complications and applying numerous hypothetical assumptions for toxicity states, incorporating inputs from theoretical complication-probability models because of limited availability of direct clinical evidence. Case numbers among study cohorts were low, and the structural design of some models inadequately reflected the natural history of HNC. Furthermore, cost inputs were incomplete and referenced historic figures.ConclusionContemporary CEMs are needed to incorporate modern estimates for toxicity risks and costs associated with PBT delivery, to provide a more accurate estimate of value, and to improve their clinical applicability with respect to PBT for HNC.
Highlights
Proton beam therapy (PBT) is increasingly used because of superior dosimetric characteristics relative to photon therapy [1,2,3,4,5,6,7,8], such as intensity-modulated radiation therapy (IMRT), resulting in lower doses and decreased complications among healthy tissues [9,10,11,12,13]. This unique advantage is welcome for the treatment of head-and-neck cancers (HNCs), anatomically complex tumors that closely approximate sensitive organs at risk
The most-common Cost-effectiveness models (CEMs) is the Markov decision-analytic model, starting with a hypothetical population with prespecified disease, prognosis, and treatment parameters. After their assigned intervention, patients will probabilistically transition through various states during posttreatment follow-up
Uncertainties in the assumptions underlying the CEMs can limit interpretation and real-world applicability. Given their increasing literature presence and consideration among modern health care evaluations, the objectives of this review are to (1) assess the reporting and methodological quality of existing cost-effectiveness studies on PBT for HNC, (2) critique the parameter inputs referenced within those models, (3) evaluate the suitability of those models for guiding clinical and economic strategies, and (4) summarize areas for improvement and future direction among successive CEMs
Summary
Proton beam therapy (PBT) is increasingly used because of superior dosimetric characteristics relative to photon therapy [1,2,3,4,5,6,7,8], such as intensity-modulated radiation therapy (IMRT), resulting in lower doses and decreased complications among healthy tissues [9,10,11,12,13] This unique advantage is welcome for the treatment of head-and-neck cancers (HNCs), anatomically complex tumors that closely approximate sensitive organs at risk. The most-common CEM is the Markov decision-analytic model, starting with a hypothetical population with prespecified disease, prognosis, and treatment parameters After their assigned intervention (eg, PBT versus IMRT), patients will probabilistically transition through various states during posttreatment follow-up
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