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Alternatives to the Kaplan–Meier estimator of progression-free survival
International Journal of Biostatistics ( IF 1.2 ) Pub Date : 2021-05-01 , DOI: 10.1515/ijb-2019-0095
Jenny J Zhang 1, 2 , Zhuoxin Sun 3, 4 , Han Yuan 5 , Molin Wang 1, 5, 6
Affiliation  

Progression-free survival (PFS), defined as the time from randomization to progression of disease or death, has been indicated as an endpoint to support accelerated approval of certain cancer drugs by the U.S. FDA. The standard Kaplan–Meier (KM) estimator of PFS, however, can result in significantly biased estimates. A major source for the bias results from the substitution of censored progression times with death times. Currently, to ameliorate this bias, several sensitivity analyses based on rather arbitrary definitions of PFS censoring are usually conducted. In addition, especially in the advanced cancer setting, patients with censored progression and observed death times have the potential to experience disease progression between those two times, in which case their true PFS time is actually between those times. In this paper, we present two alternative nonparametric estimators of PFS, which statistically incorporate survival data often available for those patients who are censored with respect to progression to obtain less biased estimates. Through extensive simulations, we show that these estimators greatly reduce the bias of the standard KM estimator and can also be utilized as alternative sensitivity analyses with a solid statistical basis in lieu of the arbitrarily defined analyses currently used. An example is also given using an ECOG-ACRIN Cancer Research Group advanced breast cancer study.

中文翻译:

无进展生存期 Kaplan-Meier 估计量的替代方案

无进展生存期 (PFS),定义为从随机化到疾病进展或死亡的时间,已被指定为支持美国 FDA 加速批准某些抗癌药物的终点。然而,PFS 的标准 Kaplan-Meier (KM) 估计会导致估计有很大偏差。偏差的一个主要来源是用死亡时间代替审查的进展时间。目前,为了改善这种偏差,通常会根据 PFS 审查的相当随意的定义进行一些敏感性分析。此外,尤其是在晚期癌症环境中,经过审查的进展和观察到的死亡时间的患者有可能在这两个时间之间经历疾病进展,在这种情况下,他们的真实 PFS 时间实际上在这两个时间之间。在本文中,我们提出了两种 PFS 的替代非参数估计量,它们在统计上整合了那些对进展进行审查以获得较少偏倚的估计值的患者的生存数据。通过广泛的模拟,我们表明这些估计量大大减少了标准 KM 估计量的偏差,并且还可以用作具有可靠统计基础的替代敏感性分析,代替目前使用的任意定义的分析。还给出了使用 ECOG-ACRIN 癌症研究组晚期乳腺癌研究的示例。我们表明,这些估计量大大减少了标准 KM 估计量的偏差,也可以用作具有可靠统计基础的替代敏感性分析,代替目前使用的任意定义的分析。还给出了使用 ECOG-ACRIN 癌症研究组晚期乳腺癌研究的示例。我们表明,这些估计量大大减少了标准 KM 估计量的偏差,也可以用作具有可靠统计基础的替代敏感性分析,代替目前使用的任意定义的分析。还给出了使用 ECOG-ACRIN 癌症研究组晚期乳腺癌研究的示例。
更新日期:2021-05-19
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