Non-parametric Analysis of Interval-Censored Survival Data with Application to a Phase III Metastatic Colorectal Cancer Clinical Trial

Yeqian Liu; Junyu Chen

doi:doi:10.11648/j.bsi.20210601.13

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Non-parametric Analysis of Interval-Censored Survival Data with Application to a Phase III Metastatic Colorectal Cancer Clinical Trial

Yeqian Liu, Junyu Chen

Published in Biomedical Statistics and Informatics (Volume 6, Issue 1)

Received: 14 February 2021 Accepted: 2 March 2021 Published: 10 March 2021

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Abstract

In oncology clinical trials, the exact time of event occurrence such as tumor progression is usually unknown but the time interval within which the event occurs is known. The determination of such survival time can be subject to measurement error and influenced by the timing of scheduled assessment. Ignoring interval-censored survival time could lead to serious estimation bias. In addition, a crucial characteristic of interval-censored data is how frequently the measurement interval is taken, which directly determine the efficiency of statistical inference. Therefore, it is highly desirable to find statistical methods that are robust to different assessment frequencies. We compare conventional imputation-based approach with non-parametric approaches to handle interval-censored survival data. We apply these approaches to both hypothesis test and the estimations of hazard and survival functions. Empirical performance of these methods are assessed through extensive simulation studies with various sample sizes. A phase III randomized clinical trial on metastatic colorectal cancer is analyzed by using conventional approaches and non-parametric interval-censored analysis approaches. Out findings suggest that the phase III colorectal cancer clinical trial failed to show a clinical benefit of adding bevacizumab (B) to standard chemotherapy (CT), and the proposed non-parametric interval-censored analysis approaches outperforms the conventional approach for routine applications to oncology clinical trials to analyze interval-censored survival data.

Published in	Biomedical Statistics and Informatics (Volume 6, Issue 1)
DOI	10.11648/j.bsi.20210601.13
Page(s)	14-22
Creative Commons	This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.
Copyright	Copyright © The Author(s), 2021. Published by Science Publishing Group

Keywords

Interval-censoring, Finkelstein’s Score Test, Generalized Log-rank Test, Non-parametric Maximum Likelihood Estimation, EM Algorithm

References

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[2]	Hess, L., Brnabic, A., Mason, O., Lee, P. & Barker, S. (2019). Relationship between Progression-free Survival and Overall Survival in Randomized Clinical Trials of Targeted and Biologic Agents in Oncology. Journal of Cancer. 10, 3717-3727.
[3]	Penson, D., Armstrong, A., Concepcion, R., Wu, K., Wang, F., Krivoshik, A., Phung, D. & Higano, C. (2016). Sensitivity analyses for progression-free survival (PFS) and radiographic PFS (rPFS) from the phase II STRIVE trial comparing enzalutamide (ENZA) with bicalutamide (BIC) in men with castration-resistant prostate cancer (CRPC). Journal of Clinical Oncology. 34, 169-169.
[4]	Turnbull, B. W. (1976). The empirical distribution function with arbitrarily grouped, censored and truncated data. Journal of the Royal Statistical Society. Series B. 38 (3): 290–295.
[5]	Gentleman, R. & Geyer, C. J. (1994). Maximum likelihood for interval censored data: Consistency and computation. Biometrika. 81 (3): 618.
[6]	Titman, A. (2017). Non-parametric maximum likelihood estimation of interval-censored failure time data subject to misclassification. Statistics and Computing. 27. 10.1007/s11222-016-9705-7.
[7]	Zhao, Q. & Sun, J. (2004). Generalized log-rank test for mixed interval-censored failure time data. Statistics in medicine. 23 (10): 1621–1629.
[8]	Sun, J., Zhao, Q. & Zhao., X. (2005). Generalized log-rank tests for interval-censored failure time data. Scandinavian Journal of Statistics. 32 (1): 49–57.
[9]	Finkelstein, D. M. (1986). A proportional hazards model for interval-censored failure time data. Biometrics. 42 (4): 845–854.
[10]	Withana, G. P., Chaudari, M., Mcmahan, C. & Kosorok, M. (2020). A proportional hazards model for interval-censored subject to instantaneous failures. Lifetime Data Analysis. 26. 10.1007/s10985-019-09467-z.
[11]	Zhang, Z. & Sun, J. (2009). Interval censoring. Statistical methods in medical research. 19, 53-70.
[12]	Sun, X. & Chen, C. (2010). Comparison of Finkelstein’s method with the conventional approach for interval-censored data analysis. Statistics in Biopharmaceutical Research. 2 (1): 97–108.
[13]	Chen, D., Sun, J. & Peace, K. E. (2002). Interval-Censored Time-to-Event Data: Methods and Applications. 10.13140/2.1.3493.2169.
[14]	Efron, B. (1977). The efficiency of Cox’s likelihood function for censored data. Journal of the American Statistical Association, 72 (359): 557–565.
[15]	Kalbfleisch, J. D. & Prentice, R. L. (2002). The statistical analysis of failure time data. Wiley-Interscience.

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APA Style

Yeqian Liu, Junyu Chen. (2021). Non-parametric Analysis of Interval-Censored Survival Data with Application to a Phase III Metastatic Colorectal Cancer Clinical Trial. Biomedical Statistics and Informatics, 6(1), 14-22. https://doi.org/10.11648/j.bsi.20210601.13

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ACS Style

Yeqian Liu; Junyu Chen. Non-parametric Analysis of Interval-Censored Survival Data with Application to a Phase III Metastatic Colorectal Cancer Clinical Trial. Biomed. Stat. Inform. 2021, 6(1), 14-22. doi: 10.11648/j.bsi.20210601.13

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AMA Style

Yeqian Liu, Junyu Chen. Non-parametric Analysis of Interval-Censored Survival Data with Application to a Phase III Metastatic Colorectal Cancer Clinical Trial. Biomed Stat Inform. 2021;6(1):14-22. doi: 10.11648/j.bsi.20210601.13

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@article{10.11648/j.bsi.20210601.13,
  author = {Yeqian Liu and Junyu Chen},
  title = {Non-parametric Analysis of Interval-Censored Survival Data with Application to a Phase III Metastatic Colorectal Cancer Clinical Trial},
  journal = {Biomedical Statistics and Informatics},
  volume = {6},
  number = {1},
  pages = {14-22},
  doi = {10.11648/j.bsi.20210601.13},
  url = {https://doi.org/10.11648/j.bsi.20210601.13},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.bsi.20210601.13},
  abstract = {In oncology clinical trials, the exact time of event occurrence such as tumor progression is usually unknown but the time interval within which the event occurs is known. The determination of such survival time can be subject to measurement error and influenced by the timing of scheduled assessment. Ignoring interval-censored survival time could lead to serious estimation bias. In addition, a crucial characteristic of interval-censored data is how frequently the measurement interval is taken, which directly determine the efficiency of statistical inference. Therefore, it is highly desirable to find statistical methods that are robust to different assessment frequencies. We compare conventional imputation-based approach with non-parametric approaches to handle interval-censored survival data. We apply these approaches to both hypothesis test and the estimations of hazard and survival functions. Empirical performance of these methods are assessed through extensive simulation studies with various sample sizes. A phase III randomized clinical trial on metastatic colorectal cancer is analyzed by using conventional approaches and non-parametric interval-censored analysis approaches. Out findings suggest that the phase III colorectal cancer clinical trial failed to show a clinical benefit of adding bevacizumab (B) to standard chemotherapy (CT), and the proposed non-parametric interval-censored analysis approaches outperforms the conventional approach for routine applications to oncology clinical trials to analyze interval-censored survival data.},
 year = {2021}
}

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TY  - JOUR
T1  - Non-parametric Analysis of Interval-Censored Survival Data with Application to a Phase III Metastatic Colorectal Cancer Clinical Trial
AU  - Yeqian Liu
AU  - Junyu Chen
Y1  - 2021/03/10
PY  - 2021
N1  - https://doi.org/10.11648/j.bsi.20210601.13
DO  - 10.11648/j.bsi.20210601.13
T2  - Biomedical Statistics and Informatics
JF  - Biomedical Statistics and Informatics
JO  - Biomedical Statistics and Informatics
SP  - 14
EP  - 22
PB  - Science Publishing Group
SN  - 2578-8728
UR  - https://doi.org/10.11648/j.bsi.20210601.13
AB  - In oncology clinical trials, the exact time of event occurrence such as tumor progression is usually unknown but the time interval within which the event occurs is known. The determination of such survival time can be subject to measurement error and influenced by the timing of scheduled assessment. Ignoring interval-censored survival time could lead to serious estimation bias. In addition, a crucial characteristic of interval-censored data is how frequently the measurement interval is taken, which directly determine the efficiency of statistical inference. Therefore, it is highly desirable to find statistical methods that are robust to different assessment frequencies. We compare conventional imputation-based approach with non-parametric approaches to handle interval-censored survival data. We apply these approaches to both hypothesis test and the estimations of hazard and survival functions. Empirical performance of these methods are assessed through extensive simulation studies with various sample sizes. A phase III randomized clinical trial on metastatic colorectal cancer is analyzed by using conventional approaches and non-parametric interval-censored analysis approaches. Out findings suggest that the phase III colorectal cancer clinical trial failed to show a clinical benefit of adding bevacizumab (B) to standard chemotherapy (CT), and the proposed non-parametric interval-censored analysis approaches outperforms the conventional approach for routine applications to oncology clinical trials to analyze interval-censored survival data.
VL  - 6
IS  - 1
ER  -

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Author Information

Yeqian Liu

Department of Mathematical Sciences, Middle Tennessee State University, Murfreesboro, USA
Junyu Chen

Department of Mathematical Sciences, Middle Tennessee State University, Murfreesboro, USA

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APA Style

Yeqian Liu, Junyu Chen. (2021). Non-parametric Analysis of Interval-Censored Survival Data with Application to a Phase III Metastatic Colorectal Cancer Clinical Trial. Biomedical Statistics and Informatics, 6(1), 14-22. https://doi.org/10.11648/j.bsi.20210601.13

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ACS Style

Yeqian Liu; Junyu Chen. Non-parametric Analysis of Interval-Censored Survival Data with Application to a Phase III Metastatic Colorectal Cancer Clinical Trial. Biomed. Stat. Inform. 2021, 6(1), 14-22. doi: 10.11648/j.bsi.20210601.13

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AMA Style

Yeqian Liu, Junyu Chen. Non-parametric Analysis of Interval-Censored Survival Data with Application to a Phase III Metastatic Colorectal Cancer Clinical Trial. Biomed Stat Inform. 2021;6(1):14-22. doi: 10.11648/j.bsi.20210601.13

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@article{10.11648/j.bsi.20210601.13,
  author = {Yeqian Liu and Junyu Chen},
  title = {Non-parametric Analysis of Interval-Censored Survival Data with Application to a Phase III Metastatic Colorectal Cancer Clinical Trial},
  journal = {Biomedical Statistics and Informatics},
  volume = {6},
  number = {1},
  pages = {14-22},
  doi = {10.11648/j.bsi.20210601.13},
  url = {https://doi.org/10.11648/j.bsi.20210601.13},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.bsi.20210601.13},
  abstract = {In oncology clinical trials, the exact time of event occurrence such as tumor progression is usually unknown but the time interval within which the event occurs is known. The determination of such survival time can be subject to measurement error and influenced by the timing of scheduled assessment. Ignoring interval-censored survival time could lead to serious estimation bias. In addition, a crucial characteristic of interval-censored data is how frequently the measurement interval is taken, which directly determine the efficiency of statistical inference. Therefore, it is highly desirable to find statistical methods that are robust to different assessment frequencies. We compare conventional imputation-based approach with non-parametric approaches to handle interval-censored survival data. We apply these approaches to both hypothesis test and the estimations of hazard and survival functions. Empirical performance of these methods are assessed through extensive simulation studies with various sample sizes. A phase III randomized clinical trial on metastatic colorectal cancer is analyzed by using conventional approaches and non-parametric interval-censored analysis approaches. Out findings suggest that the phase III colorectal cancer clinical trial failed to show a clinical benefit of adding bevacizumab (B) to standard chemotherapy (CT), and the proposed non-parametric interval-censored analysis approaches outperforms the conventional approach for routine applications to oncology clinical trials to analyze interval-censored survival data.},
 year = {2021}
}

Copy | Download

TY  - JOUR
T1  - Non-parametric Analysis of Interval-Censored Survival Data with Application to a Phase III Metastatic Colorectal Cancer Clinical Trial
AU  - Yeqian Liu
AU  - Junyu Chen
Y1  - 2021/03/10
PY  - 2021
N1  - https://doi.org/10.11648/j.bsi.20210601.13
DO  - 10.11648/j.bsi.20210601.13
T2  - Biomedical Statistics and Informatics
JF  - Biomedical Statistics and Informatics
JO  - Biomedical Statistics and Informatics
SP  - 14
EP  - 22
PB  - Science Publishing Group
SN  - 2578-8728
UR  - https://doi.org/10.11648/j.bsi.20210601.13
AB  - In oncology clinical trials, the exact time of event occurrence such as tumor progression is usually unknown but the time interval within which the event occurs is known. The determination of such survival time can be subject to measurement error and influenced by the timing of scheduled assessment. Ignoring interval-censored survival time could lead to serious estimation bias. In addition, a crucial characteristic of interval-censored data is how frequently the measurement interval is taken, which directly determine the efficiency of statistical inference. Therefore, it is highly desirable to find statistical methods that are robust to different assessment frequencies. We compare conventional imputation-based approach with non-parametric approaches to handle interval-censored survival data. We apply these approaches to both hypothesis test and the estimations of hazard and survival functions. Empirical performance of these methods are assessed through extensive simulation studies with various sample sizes. A phase III randomized clinical trial on metastatic colorectal cancer is analyzed by using conventional approaches and non-parametric interval-censored analysis approaches. Out findings suggest that the phase III colorectal cancer clinical trial failed to show a clinical benefit of adding bevacizumab (B) to standard chemotherapy (CT), and the proposed non-parametric interval-censored analysis approaches outperforms the conventional approach for routine applications to oncology clinical trials to analyze interval-censored survival data.
VL  - 6
IS  - 1
ER  -

Copy | Download