Value in health : the journal of the International Society for Pharmacoeconomics and Outcomes Research · 2026
The Use of Baseline Score in Cost-Effectiveness Modeling of Competing Interventions.
СтатьяНазвание и аннотация: Перевод в очереди
Обработка статьи · этапы, ошибки и управление
Загрузка состояния…
Ошибки и ограничения
История · было → стало
Состояние обновляется каждые 5 секунд, пока блок открыт. Процент показывается только там, где известен объём работы. Пауза останавливает продолжение этапа; текущий запрос к источнику может завершиться.
Hamza Tasnim, Chalkou Konstantina, Pellegrini Fabio, Lorscheider Johannes, Kuhle Jens, Meier Niklaus, Pletscher Mark, Egger Matthias, Salanti Georgia, Next Generation Health Technology Assessment (HTx) Work Package 2 group
Аннотация
OBJECTIVES: Network meta-analysis (NMA) with individual participant data can estimate how treatment effects change with patient characteristics. Yet cost-effectiveness analyses typically use population-average effects. We introduce a framework that incorporates NMA-derived heterogeneous treatment effects into cost-effectiveness analysis using a risk-modeling approach.
METHODS: We first derived a baseline risk score for each patient using a prognostic model. This risk score was then used as an effect modifier in a network meta-regression to estimate risk-specific treatment effects. These effects were incorporated into the cost-effectiveness model to estimate the incremental cost-effectiveness ratios and net monetary benefits as functions of the baseline risk score. We demonstrated the approach using data from observational and randomized studies in relapsing-remitting multiple sclerosis, comparing dimethyl fumarate, glatiramer acetate, and placebo.
RESULTS: Risk-dependent treatment effects from the prediction-NMR framework led to substantial variation in cost-effectiveness across the baseline risk distribution. When these treatment effects were incorporated into the cost-effectiveness model, incremental cost-effectiveness ratios increased steadily across baseline risk quintiles, from 48 811 Swiss Francs [CHF] (US $62 034) /QALY in the lowest-risk group to 212 870 CHF (US $270 536)/QALY in the highest. Dimethyl fumarate has a higher net monetary benefit up to a baseline risk of 55%, after which glatiramer acetate becomes the preferred option.
CONCLUSIONS: Our findings show that integrating baseline risk modeling with NMA and cost-effectiveness analysis provides more informative decision-making than relying on average effects. Treatment value can vary substantially across the risk spectrum, indicating that optimal therapy selection is strongly dependent on individual patient risk.
METHODS: We first derived a baseline risk score for each patient using a prognostic model. This risk score was then used as an effect modifier in a network meta-regression to estimate risk-specific treatment effects. These effects were incorporated into the cost-effectiveness model to estimate the incremental cost-effectiveness ratios and net monetary benefits as functions of the baseline risk score. We demonstrated the approach using data from observational and randomized studies in relapsing-remitting multiple sclerosis, comparing dimethyl fumarate, glatiramer acetate, and placebo.
RESULTS: Risk-dependent treatment effects from the prediction-NMR framework led to substantial variation in cost-effectiveness across the baseline risk distribution. When these treatment effects were incorporated into the cost-effectiveness model, incremental cost-effectiveness ratios increased steadily across baseline risk quintiles, from 48 811 Swiss Francs [CHF] (US $62 034) /QALY in the lowest-risk group to 212 870 CHF (US $270 536)/QALY in the highest. Dimethyl fumarate has a higher net monetary benefit up to a baseline risk of 55%, after which glatiramer acetate becomes the preferred option.
CONCLUSIONS: Our findings show that integrating baseline risk modeling with NMA and cost-effectiveness analysis provides more informative decision-making than relying on average effects. Treatment value can vary substantially across the risk spectrum, indicating that optimal therapy selection is strongly dependent on individual patient risk.