Williams Claire, Lewsey James D, Mackay Daniel F, Briggs Andrew H
Health Economics and Health Technology Assessment, Institute of Health and Wellbeing, University of Glasgow, Glasgow (CW, JDL, AHB).
Public Health, Institute of Health and Wellbeing, University of Glasgow, Glasgow (DFM).
Med Decis Making. 2017 May;37(4):427-439. doi: 10.1177/0272989X16670617. Epub 2016 Oct 4.
Modeling of clinical-effectiveness in a cost-effectiveness analysis typically involves some form of partitioned survival or Markov decision-analytic modeling. The health states progression-free, progression and death and the transitions between them are frequently of interest. With partitioned survival, progression is not modeled directly as a state; instead, time in that state is derived from the difference in area between the overall survival and the progression-free survival curves. With Markov decision-analytic modeling, a priori assumptions are often made with regard to the transitions rather than using the individual patient data directly to model them. This article compares a multi-state modeling survival regression approach to these two common methods. As a case study, we use a trial comparing rituximab in combination with fludarabine and cyclophosphamide v. fludarabine and cyclophosphamide alone for the first-line treatment of chronic lymphocytic leukemia. We calculated mean Life Years and QALYs that involved extrapolation of survival outcomes in the trial. We adapted an existing multi-state modeling approach to incorporate parametric distributions for transition hazards, to allow extrapolation. The comparison showed that, due to the different assumptions used in the different approaches, a discrepancy in results was evident. The partitioned survival and Markov decision-analytic modeling deemed the treatment cost-effective with ICERs of just over £16,000 and £13,000, respectively. However, the results with the multi-state modeling were less conclusive, with an ICER of just over £29,000. This work has illustrated that it is imperative to check whether assumptions are realistic, as different model choices can influence clinical and cost-effectiveness results.
成本效益分析中的临床疗效建模通常涉及某种形式的分区生存或马尔可夫决策分析建模。无进展、进展和死亡的健康状态以及它们之间的转变常常是研究的重点。在分区生存模型中，进展不是直接作为一个状态进行建模；相反，该状态下的时间是从总生存曲线和无进展生存曲线之间的面积差异推导出来的。在马尔可夫决策分析建模中，通常是对转变做出先验假设，而不是直接使用个体患者数据来对其进行建模。本文将一种多状态建模生存回归方法与这两种常用方法进行了比较。作为一个案例研究，我们使用了一项试验，该试验比较了利妥昔单抗联合氟达拉滨和环磷酰胺与单独使用氟达拉滨和环磷酰胺用于慢性淋巴细胞白血病一线治疗的效果。我们计算了平均生命年和质量调整生命年，这涉及到试验中生存结果的外推。我们采用了一种现有的多状态建模方法，纳入了转移风险的参数分布，以进行外推。比较结果表明，由于不同方法使用的假设不同，结果存在明显差异。分区生存模型和马尔可夫决策分析建模认为该治疗具有成本效益，增量成本效果比分别略高于16,000英镑和13,000英镑。然而，多状态建模的结果不太确定，增量成本效果比略高于29,000英镑。这项工作表明，检查假设是否现实至关重要，因为不同的模型选择会影响临床和成本效益结果。
