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Comparing and Selecting Network Meta-Analysis Models for Cost-Effectiveness Analysis of Anticancer Drugs
Mingye Zhao1,2, Taihang Shao3, Hanqiao Shao1,2
1Department of Pharmacoeconomics, School of International Pharmaceutical Business, China Pharmaceutical University, Nanjing, Jiangsu, China.
Summary
The choice of network meta-analysis (NMA) model significantly impacts oncology cost-effectiveness analysis (CEA) results, with Royston-Parmar models showing promise. Careful consideration of proportional hazards assumptions is crucial for accurate survival estimates in CEA.
Area of Science:
- Health Economics
- Biostatistics
- Pharmacoeconomics
Background:
- Network meta-analysis (NMA) models are increasingly used to inform oncology cost-effectiveness analysis (CEA).
- Survival estimates from NMAs are critical inputs for CEA, but their impact on results is not fully understood.
- Proportional hazards (PH) assumptions and extrapolation strategies can influence survival estimates and subsequent CEA outcomes.
Purpose of the Study:
- To systematically evaluate the influence of different NMA survival estimation models on oncology CEA results.
- To assess the impact of proportional hazards (PH) assumptions and extrapolation strategies on CEA outcomes.
- To identify NMA models and extrapolation methods that provide more reliable CEA results.
Main Methods:
- Evaluated 19 time-to-event NMA models including Cox-PH, fractional polynomial (FP), Royston-Parmar (RP), piecewise exponential (PWE), generalized gamma (Gengamma), and parametric survival models (PSM).
- Assessed two extrapolation strategies for non-PH models: constant-tail hazard ratio (Fixed-HR) and parametric extrapolation (Varying-HR).
- Compared CEA outcomes derived from NMA models against head-to-head clinical data using relative error (RE).
Main Results:
- NMA model selection had a greater impact on CEA outcomes than traditional parameters like costs or utilities.
- Royston-Parmar (RP) models demonstrated superior performance, while PWE, second-order FP, and Gengamma models performed comparatively worse.
- Fixed-HR extrapolation generally provided more accurate long-term projections than Varying-HR, especially under non-PH conditions.
- Violations of the PH assumption increased uncertainty, leading to cost-effectiveness conclusion reversals in up to one-third of cases.
Conclusions:
- The choice of NMA model significantly influences oncology CEA outcomes.
- Royston-Parmar (RP) models are underutilized but show strong potential for broader application in CEA.
- Fixed-HR extrapolation is preferred for its accuracy in long-term projections.
- Greater caution is needed when selecting non-PH models if the PH assumption is violated, necessitating further validation and methodological refinement in CEA guidance.
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