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Difference in Restricted Mean Survival Time for Cost-Effectiveness Analysis Using Individual Patient Data
Béranger Lueza1,2,3, Audrey Mauguen1,3, Jean-Pierre Pignon1,2,3
1Gustave Roussy, Service de biostatistique et d'épidémiologie, Villejuif, France.
Plos One
|March 10, 2016
Summary
The choice of survival analysis method significantly impacts cost-effectiveness results when estimating restricted mean survival time difference (rmstD) using individual patient data meta-analysis.
Area of Science:
- Health Economics
- Survival Analysis
- Meta-Analysis
Background:
- Restricted mean survival time difference (rmstD) is a key outcome measure in economic evaluations.
- Individual patient data (IPD) meta-analysis offers a robust approach for estimating treatment effects.
- The impact of different statistical methods on rmstD estimation within IPD meta-analysis for economic evaluation is not well-established.
Purpose of the Study:
- To illustrate how various survival analysis methods can estimate rmstD for economic evaluations using IPD meta-analysis.
- To investigate the influence of different estimation methods on cost-effectiveness results.
Main Methods:
- Utilized IPD from the Meta-Analysis of Radiotherapy in Lung Cancer, including 2,000 patients with locally advanced non-small cell lung cancer across ten trials.
- Compared methods for rmstD estimation, categorized into two approaches: direct estimation from pooled survival curves and aggregation of trial-specific rmstDs.
- Assessed the impact on incremental cost-effectiveness ratios (ICERs) and acceptability curves.
Main Results:
- The estimated rmstDs varied between 1.7 and 2.5 months.
- Mean ICERs ranged from €24,299 to €34,934 per life-year gained, demonstrating sensitivity to the chosen rmstD estimation method.
- At a €25,000 per life-year gained threshold, the probability of the experimental treatment being cost-effective ranged from 31% to 68%.
Conclusions:
- The selection of a method for estimating rmstD from IPD meta-analysis demonstrably influences cost-effectiveness analysis outcomes.
- This highlights the importance of carefully considering and reporting the chosen statistical methodology in health economic evaluations.
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