Schoenfeld David A, Finkelstein Dianne M
Massachusetts General Hospital and Harvard University, Biostatistics Unit, Boston, MA, USA.
Massachusetts General Hospital and Harvard University, Biostatistics Unit, Boston, MA, USA
Clin Trials. 2016 Jun;13(3):352-7. doi: 10.1177/1740774515625990. Epub 2016 Feb 22.
For a potentially lethal chronic disease like cancer, it is often infeasible to compare treatments on the basis of overall survival, so a combined outcome such as progression-free survival (which is the time from randomization to progression or death) has become an acceptable primary endpoint. The rationale of using an efficacy measure that is dominated by the time to progression is that an effective treatment will delay progression and when treatment is stopped at progression, the effect of treatment after this time is small. However, often trials that show a significant benefit for delaying progression but not on overall survival are not universally viewed as providing convincing evidence that the drug should become the standard of care.
We propose that when there is a significant treatment effect of delaying progression, a Bayesian analysis of overall survival should be undertaken. We suggest using a joint piecewise exponential model, where the treatment effect on the hazard for progression and for death after progression is captured through two distinct parameters. We develop a plot of the overall survival advantage of the new therapy versus the prior distribution of the relative hazard for death after progression. This plot can augment the discussion about whether the new treatment is beneficial on survival.
In the example of an early breast cancer trial for which a new treatment significantly delayed disease recurrence, our Bayesian analysis showed that with very reasonable assumptions on the effects of treatment after recurrence, there is a high probability that the new treatment improves overall survival.
For a clinical trial for which treatment delays progression, the proposed method can improve the interpretability of the survival comparison using data from the study.
对于像癌症这样具有潜在致命性的慢性疾病，基于总生存期比较治疗方法往往不可行，因此诸如无进展生存期（即从随机分组到疾病进展或死亡的时间）这样的综合结局已成为可接受的主要终点。使用以疾病进展时间为主导的疗效指标的基本原理是，有效的治疗会延迟疾病进展，并且当在疾病进展时停止治疗，此后治疗的效果很小。然而，那些显示出在延迟疾病进展方面有显著益处但在总生存期方面无显著益处的试验，通常并不被普遍视为提供了令人信服的证据表明该药物应成为标准治疗方法。
我们建议，当存在延迟疾病进展的显著治疗效果时，应进行总生存期的贝叶斯分析。我们建议使用联合分段指数模型，其中通过两个不同的参数来体现治疗对疾病进展风险和进展后死亡风险的影响。我们绘制新疗法的总生存期优势相对于进展后死亡相对风险的先验分布的图表。该图表可加强关于新治疗在生存方面是否有益的讨论。
在一项早期乳腺癌试验的例子中，一种新治疗显著延迟了疾病复发，我们的贝叶斯分析表明，在对复发后治疗效果做出非常合理假设的情况下，新治疗有很高概率能改善总生存期。
对于治疗能延迟疾病进展的临床试验，所提出的方法可以利用研究数据提高生存比较的可解释性。
