Precision Health Economics, Oakland, CA, USA.
Precision Health Economics, Oakland, CA, USA.
Value Health. 2019 Mar;22(3):332-339. doi: 10.1016/j.jval.2018.10.005. Epub 2018 Dec 14.
Payers frequently rely on budget impact model (BIM) results to help determine drug coverage policy and its effect on their bottom line. It is unclear whether BIMs typically overestimate or underestimate real-world budget impact.
We examined how different modeling assumptions influenced the results of 6 BIMs from the Institute for Clinical and Economic Review (ICER).
Retrospective analysis of pharmaceutical sales data.
From ICER reports issued before 2016, we collected estimates of 3 BIM outputs: aggregate therapy cost (ie, cost to treat the patient population with a particular therapy), therapy uptake, and price. We compared these against real-world estimates that we generated using drug sales data. We considered 2 classes of BIM estimates: those forecasting future uptake of new agents, which assumed "unmanaged uptake," and those describing the contemporaneous market state (ie, estimates of current, managed uptake and budget impact for compounds already on the market).
Differences between ICER's estimates and our own were largest for forecasted studies. Here, ICER's uptake estimates exceeded real-world estimates by factors ranging from 7.4 (sacubitril/valsartan) to 54 (hepatitis C treatments). The "unmanaged uptake" assumption (removed from ICER's approach in 2017) yields large deviations between BIM estimates and real-world consumption. Nevertheless, in some cases, ICER's BIMs that relied on current market estimates also deviated substantially from real-world sales data.
This study highlights challenges with forecasting budget impact. In particular, assumptions about uptake and data source selection can greatly influence the accuracy of results.
支付方经常依赖预算影响模型（BIM）的结果来帮助确定药物覆盖政策及其对底线的影响。目前尚不清楚 BIM 是否通常高估或低估实际的预算影响。
我们研究了不同的建模假设如何影响来自临床与经济评价研究所（ICER）的 6 个 BIM 的结果。
对药物销售数据进行回顾性分析。
从 2016 年之前发布的 ICER 报告中，我们收集了 3 个 BIM 输出的估计值：总体治疗成本（即，用特定疗法治疗患者人群的成本）、治疗采用率和价格。我们将这些估计值与我们使用药物销售数据生成的实际估计值进行了比较。我们考虑了 2 类 BIM 估计值：预测新药物未来采用率的估计值，假设“无管理采用率”，以及描述同期市场状态的估计值（即，已上市药物的当前、管理采用率和预算影响的估计值）。
ICER 的估计值与我们自己的估计值之间的差异在预测研究中最大。在这里，ICER 的采用率估计值比实际估计值高出 7.4 倍（沙库巴曲缬沙坦）到 54 倍（丙型肝炎治疗）。“无管理采用率”假设（2017 年从 ICER 方法中删除）导致 BIM 估计值与实际消费之间存在较大偏差。尽管如此，在某些情况下，ICER 依赖当前市场估计的 BIM 也与实际销售数据有很大差异。
本研究强调了预测预算影响的挑战。特别是，采用率和数据源选择的假设会极大地影响结果的准确性。
