Cranmer Holly L, Shields Gemma E, Bullement Ash
Takeda UK, London, UK.
Division of Population Health, Health Services Research, and Primary Care, Faculty of Biology, Medicine and Health, School of Health Sciences, Manchester Centre for Health Economics, University of Manchester, Manchester, UK.
Appl Health Econ Health Policy. 2023 May;21(3):385-394. doi: 10.1007/s40258-023-00792-x. Epub 2023 Feb 27.
A common challenge in health technology assessments (HTAs) of cancer treatments is how subsequent therapy use within the trial follow-up may influence cost-effectiveness model outcomes. Although overall survival (OS) is often a key driver of model results, there are no guidelines to advise how to adjust for this potential confounding, with different approaches available dependent on the model structure.
We compared a partitioned survival analysis (PartSA) with a semi-Markov multi-state model (MSM) structure, with and without attempts to adjust for the impact of subsequent therapies on OS using a case study describing outcomes for people with relapsed/refractory multiple myeloma.
Both model structures included three health states: pre-progression, progressed disease and death. Three traditional crossover methods were considered within the context of the PartSA, whereas for the MSM, the probability of post-progression death was pooled across arms. Impacts on the model incremental cost-effectiveness ratio (ICER) were recorded.
The unadjusted PartSA produced an ICER of £623,563, and after adjustment yielded an ICER range of £381,340-£386,907. The unadjusted MSM produced an ICER of £1,283,780. Adjusting OS in the MSM resulted in an ICER of £345,486.
The simplicity of the PartSA is lost when the decision problem becomes more complex (for example, when OS data are confounded by subsequent therapies). In this setting, the MSM structure may be considered more flexible, with fewer and less restrictive assumptions required versus the PartSA. Researchers should consider important study design features that may influence the generalisability of data when undertaking model conceptualisation.
癌症治疗的卫生技术评估（HTA）中的一个常见挑战是，试验随访期间后续治疗的使用如何影响成本效益模型的结果。尽管总生存期（OS）通常是模型结果的关键驱动因素，但对于如何针对这种潜在的混杂因素进行调整，尚无指导原则，具体方法因模型结构而异。
我们通过一个描述复发/难治性多发性骨髓瘤患者结局的案例研究，比较了分区生存分析（PartSA）与半马尔可夫多状态模型（MSM）结构，同时考虑了是否尝试针对后续治疗对OS的影响进行调整。
两种模型结构均包括三个健康状态：进展前、疾病进展和死亡。在PartSA的背景下考虑了三种传统的交叉方法，而对于MSM，进展后死亡的概率在各治疗组中进行汇总。记录对模型增量成本效益比（ICER）的影响。
未调整的PartSA得出的ICER为623,563英镑，调整后得出的ICER范围为381,340 - 386,907英镑。未调整的MSM得出的ICER为1,283,780英镑。在MSM中调整OS后得出的ICER为345,486英镑。
当决策问题变得更加复杂时（例如，当OS数据受到后续治疗的混杂影响时），PartSA的简单性就会丧失。在这种情况下，MSM结构可能被认为更灵活，与PartSA相比，所需的假设更少且限制更少。研究人员在进行模型概念化时应考虑可能影响数据通用性的重要研究设计特征。
