Stone Biostatistics Ltd, Crewe, UK.
Department of Biostatistics and Research Methodology, NHMRC Clinical Trials Centre, University of Sydney, Sydney, Australia.
Ann Oncol. 2019 Feb 1;30(2):332-338. doi: 10.1093/annonc/mdy514.
Recent published studies have shown meaningful discrepancies between local investigator and blinded, independent, central review (BICR) assessed median progression-free survival (PFS). When the local review but not BICR shows progression, generally, no further assessments are carried out and patients are censored in the BICR analysis, leading to violation of the statistical assumptions of independence between censoring and outcome used in survival analysis methods.
We carried out a simulation study to assess methodological reasons behind these discrepancies and corroborated our findings in a case study of three BRCA-mutated ovarian cancer trials. We briefly outline possible methodological solutions that may lead to improved estimation of the BICR medians.
The Kaplan-Meier (KM) curve for the BICR PFS can often be exaggerated. The degree of bias is largest when there is reasonably strong correlation between BICR and local PFS, especially when PFS is long compared with assessment frequency. This can result in an exaggeration of the medians and their difference; however, the hazard ratio (HR) is much less susceptible to bias. Our simulation shows that when the true BICR median PFS was 19 months, and patients assessed every 12 weeks, the estimated KM curves were materially biased whenever the correlation between BICR and local PFS was 0.4 or greater. This was corroborated by case studies where, in the active arm, the BICR median PFS was between 6 and 11 months greater than the local median PFS. Further research is required to find improved methods for estimating BICR survival curves.
In general, when there is a difference between local and BICR medians, the true BICR KM curve is likely to be exaggerated and its true median will probably lie somewhere between the observed local and BICR medians. Presentation of data should always include both BICR and local results whenever a BICR is carried out.
最近发表的研究表明,局部研究者和盲法、独立、中心评估(BICR)评估的中位无进展生存期(PFS)之间存在显著差异。当局部评估但 BICR 未显示进展时,通常不会进行进一步评估,患者在 BICR 分析中被删失,导致生存分析方法中删失和结局之间独立性的统计假设被违反。
我们进行了一项模拟研究,以评估这些差异背后的方法学原因,并在三个 BRCA 突变卵巢癌试验的案例研究中验证了我们的发现。我们简要概述了可能导致 BICR 中位数更好估计的方法学解决方案。
BICR PFS 的 Kaplan-Meier(KM)曲线经常被夸大。当 BICR 和局部 PFS 之间存在相当强的相关性时,偏倚程度最大,尤其是当 PFS 比评估频率长时。这会导致中位数及其差异的夸大;然而,风险比(HR)受偏倚的影响要小得多。我们的模拟表明,当真实的 BICR 中位 PFS 为 19 个月,且患者每 12 周评估一次时,当 BICR 和局部 PFS 之间的相关性为 0.4 或更大时,估计的 KM 曲线存在明显的偏倚。这在案例研究中得到了证实,在活性臂中,BICR 中位 PFS 比局部中位 PFS 长 6 到 11 个月。需要进一步研究以找到改进的方法来估计 BICR 生存曲线。
一般来说,当局部和 BICR 中位数存在差异时,真实的 BICR KM 曲线可能被夸大,其真实中位数可能介于观察到的局部和 BICR 中位数之间。只要进行了 BICR,就应始终同时呈现 BICR 和局部结果。