Effect of right censoring bias on survival analysis.
Effect of right censoring bias on survival analysis.
复制标题
右审查偏差对生存分析的影响。
DOI：
10.1200/jco.2019.37.15_suppl.e18188
复制
发表时间：
2019
影响因子：
45.3
通讯作者：
Laura Barrajon
中科院分区：
文献类型：
--
作者：
Enrique Barraj'on;Laura Barrajon
e18188背景：生存期Kaplan-Meier分析代表了肿瘤学治疗有效性的最客观指标，尽管在精准医学时代存在令人担忧的潜在偏倚。与临床试验设计固有的偏倚无关，偏倚可能是患者删失或不完整观察的结果。与无疾病/进展生存期不同，总生存期基于明确定义的时间点，因此避免了间隔删失，但我们认为，由于随访不完整，正确的删失仍可能是偏倚的来源。研究方法：使用R 3.5.1版语言和集成开发环境RStudio，使用生存包及其可用数据集进行模拟和生存分析。根据Weibull模型模拟生存时间，该模型具有2个参数（形状和尺度），可确定每个病例的事件时间。三种类型的权利审查机制被认为是独立的和分析：1）病例删失，其中删失随机数量的病例，并且由此产生的生存时间缩短随机量，2）时间删失，其中当且仅当随机删失时间变量短于事件时间时才应用随机删失时间变量，以及3）中期删失，其中，随机时间变量确定自试验开始以来的病例纳入时间，固定截止时间确定是否对每个病例进行删失（如果截止时间短于纳入时间加上事件时间）。对于每种删失机制，使用1000个未删失病例组和1000个删失病例组模拟100项试验，以便估计每项试验的删失考克斯风险比（cHR）。一个互动的应用程序显示正确的审查效果。结果：根据事件和删失病例的生存时间建立偏倚指数（BI）。病例删失的BI（平均值= 1.75，SD = 0.29）高于时间删失（平均值= 1.15，SD = 0.19，p = 2.02 e-30）和中期删失（平均值= 0.72，SD = 0.21，p = 3.46 e-34）。发现在病例删失中，删失比例与cHR呈负相关（r = -0.86）。在所有可用的数据集中，退伍军人管理局肺癌研究显示偏倚为1.83，表明两个治疗组的病例删失偏倚。结论：根据本研究的结果，建议：1）最终结果应包括定义的关注期内的所有事件，2）偏倚指数可能有助于检测潜在偏倚并纠正估计的生存期。最近的临床试验计划进行删失偏倚分析。
e18188 Background: Survival Kaplan-Meier analysis represents the most objective measure of treatment efficacy in oncology, though subjected to potential bias which is worrisome in an era of precision medicine. Independent of the bias inherent to the design of clinical trials, bias may be the result of patient censoring, or incomplete observation. Unlike disease/progression free survival, overall survival is based on a well defined time point and thus avoids interval censoring, but it is our claim that right censoring, due to incomplete follow-up, may still be a source of bias. Methods: The R version 3.5.1 language and the integrated development environment RStudio were used for simulations and survival analysis with the survival package and their available datasets . Survival time was simulated according to a Weibull model with 2 parameters, shape and scale, that determine the event time for every case. Three types of right censoring mechanisms are considered and analyzed independently: 1) case censoring, in which a random number of cases are censored, and the resulting survival time is shortened by a random amount, 2) time censoring, in which a random censoring time variable is applied if and only if it is shorter than the event time, and 3) interim censoring, where a random time variable determines the case inclusion time since the start of trial, and a fixed cutt-off time determines if every case is censored (if the cutt-off time is shorter the the inclusion time plus the event time) or not. For every censoring mechanism, 100 trials was simulated with a 1000 uncensored cases arm and 1000 censored cases arm, in such a way that a censoring Cox hazard ratio (cHR) may be estimated for every trial. An interactive app showing the right censoring effect is presented. Results: A bias index (BI) was buit based on the survival time of event and censored cases. Case censoring was associated with higher BI (mean = 1.75, SD = 0.29) than time censoring (mean = 1.15, SD = 0.19, p = 2.02e-30) and interim censoring (mean = 0.72, SD = 0.21, p = 3.46e-34). It was found an inverse relationship between the censoring proportion and the cHR in case censoring (r = -0.86). Of all the available datasets, the Veterans' Administration Lung Cancer study showed a bias of 1.83, suggesting case censoring bias in both treatment arms. Conclusions: Based in the results of this study it is suggested that: 1) Final results should include all the events in the defined period of interest, 2) a bias index may help in detecting potential bias and correct estimated survival. Censoring bias analysis is planned in recent clinical trials.