Handels Ron, Herring William L, Kamgar Farzam, Aye Sandar, Tate Ashley, Green Colin, Gustavsson Anders, Wimo Anders, Winblad Bengt, Sköldunger Anders, Raket Lars Lau, Stellick Chelsea Bedrejo, Spackman Eldon, Hlávka Jakub, Wei Yifan, Mar Javier, Soto-Gordoa Myriam, de Kok Inge, Brück Chiara, Anderson Robert, Pemberton-Ross Peter, Urbich Michael, Jönsson Linus
Alzheimer Centre Limburg, Faculty of Health Medicine and Life Sciences, School for Mental Health and Neuroscience, Department of Psychiatry and Neuropsychology, Maastricht University, Maastricht, The Netherlands; Division of Neurogeriatrics, Department of Neurobiology, Care Sciences and Society, Karolinska Institutet, Solna, Sweden.
Division of Neurogeriatrics, Department of Neurobiology, Care Sciences and Society, Karolinska Institutet, Solna, Sweden; Health Economics, RTI Health Solutions, Research Triangle Park, NC, USA.
Value Health. 2025 Apr;28(4):497-510. doi: 10.1016/j.jval.2024.09.006. Epub 2024 Oct 8.
Decision-analytic models assessing the value of emerging Alzheimer's disease (AD) treatments are challenged by limited evidence on short-term trial outcomes and uncertainty in extrapolating long-term patient-relevant outcomes. To improve understanding and foster transparency and credibility in modeling methods, we cross-compared AD decision models in a hypothetical context of disease-modifying treatment for mild cognitive impairment (MCI) due to AD.
A benchmark scenario (US setting) was used with target population MCI due to AD and a set of synthetically generated hypothetical trial efficacy estimates. Treatment costs were excluded. Model predictions (10-year horizon) were assessed and discussed during a 2-day workshop.
Nine modeling groups provided model predictions. Implementation of treatment effectiveness varied across models based on trial efficacy outcome selection (clinical dementia rating - sum of boxes, clinical dementia rating - global, mini-mental state examination, functional activities questionnaire) and analysis method (observed severity transitions, change from baseline, progression hazard ratio, or calibration to these). Predicted mean time in MCI ranged from 2.6 to 5.2 years for control strategy and from 0.1 to 1.0 years for difference between intervention and control strategies. Predicted quality-adjusted life-year gains ranged from 0.0 to 0.6 and incremental costs (excluding treatment costs) from -US$66 897 to US$11 896.
Trial data can be implemented in different ways across health-economic models leading to large variation in model predictions. We recommend (1) addressing the choice of outcome measure and treatment effectiveness assumptions in sensitivity analysis, (2) a standardized reporting table for model predictions, and (3) exploring the use of registries for future AD treatments measuring long-term disease progression to reduce uncertainty of extrapolating short-term trial results by health-economic models.
评估新兴阿尔茨海默病(AD)治疗价值的决策分析模型面临着短期试验结果证据有限以及推断长期患者相关结果存在不确定性的挑战。为了增进对建模方法的理解,提高透明度和可信度,我们在因AD导致轻度认知障碍(MCI)的疾病修饰治疗这一假设背景下,对AD决策模型进行了交叉比较。
采用一个基准情景(美国背景),目标人群为因AD导致的MCI,并使用了一组综合生成的假设试验疗效估计值。排除了治疗成本。在为期两天的研讨会上对模型预测(10年时间范围)进行了评估和讨论。
九个建模小组提供了模型预测。基于试验疗效结果选择(临床痴呆评定量表 - 框项总和、临床痴呆评定量表 - 总体、简易精神状态检查表、功能活动问卷)和分析方法(观察到的严重程度转变、相对于基线的变化、进展风险比或对这些的校准),各模型在治疗效果的实施方面存在差异。对于对照策略,预测的MCI平均时长为2.6至5.2年,干预与对照策略之间的差异为0.1至1.0年。预测的质量调整生命年增益范围为0.0至0.6,增量成本(不包括治疗成本)范围为 - 66897美元至11896美元。
试验数据在不同的健康经济模型中可以有不同的实施方式,导致模型预测存在很大差异。我们建议:(1)在敏感性分析中处理结局指标的选择和治疗效果假设;(2)为模型预测制定标准化报告表;(3)探索使用登记系统来衡量未来AD治疗的长期疾病进展,以减少健康经济模型外推短期试验结果的不确定性。