Chansky Kari, Subotic Dragan, Foster Nathan R, Blum Torsten
Cancer Research and Biostatistics, Seattle, USA.
Clinic for Thoracic Surgery, Clinical Center of Serbia, University of Belgrade School of Medicine, Belgrade, Serbia.
J Thorac Dis. 2016 Nov;8(11):3457-3463. doi: 10.21037/jtd.2016.11.28.
Although survival analyses represent one of the cornerstones in oncology in general, some aspects of the reported survival data in lung cancer patients are still not fully elucidated.
After having defined several open questions, an evidence based approach was applied in order to answer these questions. Areas of interest were: (I) possible uncertainties in reported survival data; (II) survival surrogates; (III) recommended methods for evaluating progression free survival (PFS) as a surrogate endpoint in future datasets; (IV) postoperative lung cancer recurrence and survival.
In recent years, PFS has seen increasing use as a primary endpoint, particularly in phase II trials. This article focuses on the statistical aspects, and particularly on evaluating the ability of PFS to accurately predict the overall survival (OS) outcome. If the data are available from randomized trials, then the evaluation of trial level surrogacy should be carried out, in addition to the methods described in the paper. If it is not a case, the patient-level methods should be applied. Suggestions for "landmark analysis" are also given: (I) classify your cases according to progression status (progressed, progression-free, or unknown) at one or more time points of interest; (II) perform a separate Cox proportional hazards regression analysis for each time point; (III) determine and report the landmark time point where progression status best predicts survival according to the hazard ratios and P values; (IV) calculate the concordance index for each landmark analysis model. The concordance index (or "c-Index") is essentially the probability that for any two randomly selected cases, the case that is predicted to have the worst outcome, does in fact have the worst outcome.
the widening spectrum of diagnostic and treatment in pulmonary oncology imposes the need for an updated knowledge about statistical method that would fit best for the analysed problem.
尽管生存分析总体上是肿瘤学的基石之一，但肺癌患者报告的生存数据的某些方面仍未得到充分阐明。
在确定了几个未解决的问题后，采用了基于证据的方法来回答这些问题。感兴趣的领域包括：（I）报告的生存数据中可能存在的不确定性；（II）生存替代指标；（III）评估无进展生存期（PFS）作为未来数据集中替代终点的推荐方法；（IV）肺癌术后复发与生存。
近年来，PFS越来越多地被用作主要终点，尤其是在II期试验中。本文重点关注统计方面，特别是评估PFS准确预测总生存期（OS）结果的能力。如果有随机试验的数据，除了本文所述方法外，还应进行试验水平替代指标的评估。如果不是这种情况，则应应用患者水平的方法。还给出了“标志性分析”的建议：（I）在一个或多个感兴趣的时间点根据进展状态（进展、无进展或未知）对病例进行分类；（II）对每个时间点进行单独的Cox比例风险回归分析；（III）根据风险比和P值确定并报告进展状态最能预测生存的标志性时间点；（IV）计算每个标志性分析模型的一致性指数。一致性指数（或“c指数”）本质上是对于任意两个随机选择的病例，预测结果最差的病例实际上结果最差的概率。
肺部肿瘤学中诊断和治疗范围的不断扩大，使得有必要更新关于最适合分析问题的统计方法的知识。
