On assessing survival benefit of immunotherapy using long-term restricted mean survival time

被引:2
作者
Horiguchi, Miki [1 ,2 ]
Tian, Lu [3 ]
Uno, Hajime [1 ,2 ,4 ]
机构
[1] Dana Farber Canc Inst, Dept Data Sci, Boston, MA USA
[2] Dana Farber Canc Inst, Dept Med Oncol, Div Populat Sci, Boston, MA USA
[3] Stanford Univ, Sch Med, Dept Biomed Data Sci, Palo Alto, CA USA
[4] Dana Farber Canc Inst, Dept Data Sci, Boston, MA 02215 USA
基金
美国国家卫生研究院;
关键词
delayed difference; hazard ratio; non-proportional hazards; versatile test; weighted logrank test; TRIALS; DIFFERENCE;
D O I
10.1002/sim.9662
中图分类号
Q [生物科学];
学科分类号
07 ; 0710 ; 09 ;
摘要
The pattern of the difference between two survival curves we often observe in randomized clinical trials for evaluating immunotherapy is not proportional hazards; the treatment effect typically appears several months after the initiation of the treatment (ie, delayed difference pattern). The commonly used logrank test and hazard ratio estimation approach will be suboptimal concerning testing and estimation for those trials. The long-term restricted mean survival time (LT-RMST) approach is a promising alternative for detecting the treatment effect that potentially appears later in the study. A challenge in employing the LT-RMST approach is that it must specify a lower end of the time window in addition to a truncation time point that the RMST requires. There are several investigations and suggestions regarding the choice of the truncation time point for the RMST. However, little has been investigated to address the choice of the lower end of the time window. In this paper, we propose a flexible LT-RMST-based test/estimation approach that does not require users to specify a lower end of the time window. Numerical studies demonstrated that the potential power loss by adopting this flexibility was minimal, compared to the standard LT-RMST approach using a prespecified lower end of the time window. The proposed method is flexible and can offer higher power than the RMST-based approach when the delayed treatment effect is expected. Also, it provides a robust estimate of the magnitude of the treatment effect and its confidence interval that corresponds to the test result.
引用
收藏
页码:1139 / 1155
页数:17
相关论文
共 50 条
  • [41] Restricted mean survival time: an alternative to the hazard ratio for the design and analysis of randomized trials with a time-to-event outcome
    Patrick Royston
    Mahesh KB Parmar
    [J]. BMC Medical Research Methodology, 13
  • [42] Restricted mean survival time: an alternative to the hazard ratio for the design and analysis of randomized trials with a time-to-event outcome
    Royston, Patrick
    Parmar, Mahesh K. B.
    [J]. BMC MEDICAL RESEARCH METHODOLOGY, 2013, 13
  • [43] Window mean survival time
    Paukner, Mitchell
    Chappell, Richard
    [J]. STATISTICS IN MEDICINE, 2021, 40 (25) : 5521 - 5533
  • [44] Versatile tests for window mean survival time
    Paukner, Mitchell
    Chappell, Richard
    [J]. STATISTICS IN MEDICINE, 2022, 41 (19) : 3720 - 3736
  • [45] Covariate-Adjusted Restricted Mean Survival Times and Curves Reply
    Trinquart, Ludovic
    Jacot, Justine
    Conner, Sarah C.
    Porcher, Raphael
    [J]. JOURNAL OF CLINICAL ONCOLOGY, 2017, 35 (04) : 466 - +
  • [46] Impact of Overdiagnosis on Long-Term Breast Cancer Survival
    Fann, Jean Ching-Yuan
    Chang, King-Jen
    Hsu, Chen-Yang
    Yen, Amy Ming-Fang
    Yu, Cheng-Ping
    Chen, Sam Li-Sheng
    Kuo, Wen-Hung
    Tabar, Laszlo
    Chen, Hsiu-Hsi
    [J]. CANCERS, 2019, 11 (03)
  • [47] What is the best method for long-term survival analysis?
    Bekiroglu, G. Nural
    Avci, Esin
    Ozgur, Emrah
    [J]. INDIAN JOURNAL OF CANCER, 2022, 59 (04) : 457 - 461
  • [48] Pancreatic cancer; reporting and long-term survival in Sweden
    Lambe, Mats
    Eloranta, Sandra
    Wigertz, Annette
    Blomqvist, Paul
    [J]. ACTA ONCOLOGICA, 2011, 50 (08) : 1220 - 1227
  • [49] Predicting the restricted mean event time with the subject's baseline covariates in survival analysis
    Tian, Lu
    Zhao, Lihui
    Wei, L. J.
    [J]. BIOSTATISTICS, 2014, 15 (02) : 222 - 233
  • [50] Adjusted restricted mean survival times in observational studies
    Conner, Sarah C.
    Sullivan, Lisa M.
    Benjamin, Emelia J.
    LaValley, Michael P.
    Galea, Sandro
    Trinquart, Ludovic
    [J]. STATISTICS IN MEDICINE, 2019, 38 (20) : 3832 - 3860