Division of Pharmacoepidemiology & Clinical Pharmacology, Utrecht Institute for Pharmaceutical Sciences (UIPS), Utrecht University, Universiteitsweg 99, 3584 CG, Utrecht, The Netherlands.
National Health Care Institute, Eekholt 4, 1112 XH, Diemen, The Netherlands.
Eur J Health Econ. 2020 Aug;21(6):845-853. doi: 10.1007/s10198-020-01176-x. Epub 2020 Apr 4.
High budget impact (BI) estimates of new drugs have led to decision-making challenges potentially resulting in restrictions in patient access. However, current BI predictions are rather inaccurate and short term. We therefore developed a new approach for BI prediction. Here, we describe the validation of our BI prediction approach using oncology drugs as a case study.
We used Dutch population-level data to estimate BI where BI is defined as list price multiplied by volume. We included drugs in the antineoplastic agents ATC category which the European Medicines Agency (EMA) considered a New Active Substance and received EMA marketing authorization (MA) between 2000 and 2017. A mixed-effects model was used for prediction and included tumor site, orphan, first in class or conditional approval designation as covariates. Data from 2000 to 2012 were the training set. BI was predicted monthly from 0 to 45 months after MA. Cross-validation was performed using a rolling forecasting origin with e^|Ln(observed BI/predicted BI)| as outcome.
The training set and validation set included 25 and 44 products, respectively. Mean error, composed of all validation outcomes, was 2.94 (median 1.57). Errors are higher with less available data and at more future predictions. Highest errors occur without any prior data. From 10 months onward, error remains constant.
The validation shows that the method can relatively accurately predict BI. For payers or policymakers, this approach can yield a valuable addition to current BI predictions due to its ease of use, independence of indications and ability to update predictions to the most recent data.
新药物的高预算影响（BI）估计导致了决策挑战，可能导致患者获得药物的机会受限。然而，目前的 BI 预测准确性和时效性都较差。因此，我们开发了一种新的 BI 预测方法。本文通过肿瘤药物的案例研究来验证 BI 预测方法。
我们使用荷兰人群水平数据来估计 BI，BI 定义为标价乘以销量。我们纳入了欧洲药品管理局（EMA）认为是新活性物质且在 2000 年至 2017 年期间获得 EMA 上市许可（MA）的抗肿瘤药物 ATC 类别中的药物。采用混合效应模型进行预测，并将肿瘤部位、孤儿药、同类首创药物或有条件批准的指定作为协变量。2000 年至 2012 年的数据作为训练集。MA 后 0 至 45 个月内每月预测 BI。采用滚动预测原点，以观测 BI/预测 BI 的对数的绝对值（|Ln(observed BI/predicted BI)|）作为交叉验证的结果。
训练集和验证集分别包括 25 个和 44 个产品。由所有验证结果组成的平均误差为 2.94（中位数为 1.57）。可用数据越少，预测的未来时间越长，误差越大。在没有任何先前数据的情况下，误差最大。从 10 个月开始，误差保持稳定。
验证表明该方法可以相对准确地预测 BI。对于支付方或政策制定者而言，由于该方法易于使用、不依赖于适应证以及能够根据最新数据更新预测，因此该方法可作为当前 BI 预测的有益补充。
