Department of Radiation Oncology, Vanderbilt University Medical Center, United States.
Department of Radiation Oncology, Vanderbilt University Medical Center, United States.
Lung Cancer. 2021 Jul;157:75-78. doi: 10.1016/j.lungcan.2021.04.019. Epub 2021 Apr 26.
PURPOSE/BACKGROUND: Immortal time bias (ITB) can hinder appropriate interpretations of studies administering adjuvant therapies. Given the increase in National Cancer Data Base (NCDB) analyses evaluating postoperative radiation therapy (PORT) as an adjuvant therapy, we sought to practically demonstrate the effects of ITB by performing a series of simulated NCDB analyses.
A simulated NCDB analysis was performed to examine how the reported benefit of PORT in stage III non-small cell lung cancer (NSCLC) may change with adjustment for ITB utilizing sequential land mark analysis (SLMA) and time dependent Cox (TDC) modeling.
On the simulation analysis of 6440 NSCLC patients, we found that the omission of PORT without ITB adjustment was associated with an increased risk of death (HR 1.17, p < 0.0001). After performing a sequential LMA, the detrmient of omitting PORT continued to decrease until it was no longer significant at 8 months, HR 1.05 (p = 0.09). With the TDC model, although still significant, the relative benefit of PORT decreased, to a HR of 1.07 (p = 0.02).
Immortal time bias can alter the results of survival analyses if not carefully accounted for. Adjusting for this bias is essential for accurate data interpretation and to better quantify the impact and effect size of adjuvant therapies such as PORT.
目的/背景: Immortal time bias(ITB)可能会影响辅助治疗研究的合理解读。鉴于越来越多的 National Cancer Data Base(NCDB)分析评估术后放射治疗(PORT)作为辅助治疗,我们希望通过一系列模拟 NCDB 分析来实际展示 ITB 的影响。
进行了模拟 NCDB 分析,以利用序贯 landmark 分析(SLMA)和时间依赖 Cox(TDC)建模来检查在调整 ITB 后,PORT 在 III 期非小细胞肺癌(NSCLC)中的获益可能如何变化。
在对 6440 例 NSCLC 患者的模拟分析中,我们发现,如果不进行 ITB 调整而省略 PORT,则与死亡风险增加相关(HR 1.17,p<0.0001)。在进行序贯 LMA 后,省略 PORT 的不利影响继续下降,直到 8 个月时不再显著,HR 为 1.05(p=0.09)。使用 TDC 模型,虽然仍然显著,但 PORT 的相对获益减少,HR 为 1.07(p=0.02)。
如果不仔细考虑, Immortal time bias 可能会改变生存分析的结果。调整这种偏差对于准确的数据解释以及更准确地量化辅助治疗(如 PORT)的影响和效果大小至关重要。