Adjusting Survival Time Estimates to Account for Treatment Switching in Randomized Controlled Trials-an Economic Evaluation Context: Methods, Limitations, and Recommendations
Adjusting Survival Time Estimates to Account for Treatment Switching in Randomized Controlled Trials-an Economic Evaluation Context: Methods, Limitations, and Recommendations
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DOI：
10.1177/0272989x13520192
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发表时间：
2014-04-01
影响因子：
3.6
通讯作者：
Campbell, Michael J.
中科院分区：
文献类型：
--
作者：
Latimer, Nicholas R.;Abrams, Keith R.;Campbell, Michael J.
背景治疗转换通常发生在晚期或转移性癌症背景下的新型干预措施的临床试验中。然而，在卫生技术评估（HTAs）中，调整转换的方法并不一致，而且可能不适当。Objective.我们提出的建议，使用的方法来调整生存估计存在的治疗转换的背景下，经济评估。方法.我们提供了治疗转换问题的背景，并总结了用于调整HTAs的方法。我们讨论了与调整方法相关的假设和限制，并利用模拟研究的结果对其使用提出建议。结果我们证明了用于调整治疗切换的方法具有重要的局限性，并且在现实情况下经常产生偏差。我们提出了一个分析框架，旨在增加合适的调整方法，可以在个案的基础上确定的概率。我们建议，临床试验的特点，以及在其中观察到的治疗转换机制，应与调整方法的关键假设一起考虑。关键假设包括与截尾权重逆概率（IPCW）方法相关的无未测量混杂因素假设和与秩保持结构失效时间模型（RPSFTM）相关的共同治疗效应假设。结论.与RPSFTM和IPCW等切换调整方法相关的限制意味着它们适用于不同的场景。在某些情况下，两种方法都可能存在偏倚;应考虑2阶段方法，意向治疗分析有时可能产生最小偏倚。调整方法的数据要求对临床试验者也有重要意义。
Background. Treatment switching commonly occurs in clinical trials of novel interventions in the advanced or metastatic cancer setting. However, methods to adjust for switching have been used inconsistently and potentially inappropriately in health technology assessments (HTAs). Objective. We present recommendations on the use of methods to adjust survival estimates in the presence of treatment switching in the context of economic evaluations. Methods. We provide background on the treatment switching issue and summarize methods used to adjust for it in HTAs. We discuss the assumptions and limitations associated with adjustment methods and draw on results of a simulation study to make recommendations on their use. Results. We demonstrate that methods used to adjust for treatment switching have important limitations and often produce bias in realistic scenarios. We present an analysis framework that aims to increase the probability that suitable adjustment methods can be identified on a case-by-case basis. We recommend that the characteristics of clinical trials, and the treatment switching mechanism observed within them, should be considered alongside the key assumptions of the adjustment methods. Key assumptions include the no unmeasured confounders assumption associated with the inverse probability of censoring weights (IPCW) method and the common treatment effect assumption associated with the rank preserving structural failure time model (RPSFTM). Conclusions. The limitations associated with switching adjustment methods such as the RPSFTM and IPCW mean that they are appropriate in different scenarios. In some scenarios, both methods may be prone to bias; 2-stage methods should be considered, and intention-to-treat analyses may sometimes produce the least bias. The data requirements of adjustment methods also have important implications for clinical trialists.