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Background The data from immuno-oncology therapy trials often show delayed effects, cure rate, crossing hazards, or some mixture of these phenomena. Thus, the common assumption of proportional hazards is often violated such that the commonly used log-rank test can be very underpowered. In these trials, the conventional hazard ratio for describing the treatment effect may not be a good estimand due to the lack of an easily understandable interpretation. To overcome this challenge, restricted mean survival time (RMST) has been strongly recommended for survival analysis in clinical literature due to its independence of the proportional hazard assumption as well as a more clinically meaningful interpretation. The RMST also aligns well with the estimand associated with the analysis from the recommendation in ICH E-9 (R1), and the test/estimation coherency. Currently, the Kaplan Meier (KM) curve is commonly applied to RMST related analyses. Due to some drawbacks of the KM approach such as the limitation in extrapolating to time points beyond the follow-up time, and the large variance at time points with small numbers of events, the RMST may be hindered.
Methods To fully enhance the RMST method, the survival curve using a mixture model is proposed in this paper to construct dynamic RMST curves to evaluate and monitor survival analysis in clinical trials.
Results This new dynamic RMST curve overcomes the drawbacks from the KM approach. The good performance of this new proposal is illustrated through three real examples.
Conclusions RMST provides a clinically meaningful and easily interpretable measure for survival clinical trials. The proposed dynamic RMST approach provides a useful tool for assessing treatment effect over different time frames for survival clinical trials. This dynamic RMST curve can also be useful for checking whether the follow-up time for a study is long enough to demonstrate a treatment difference. The prediction feature of the dynamic RMST analysis may also be used for determining an appropriate time point for an interim analysis , and an evaluation tool for study recommendation from DMC .
Keywords
Immuno-oncology trial, Survival, RMST, mixture Weibull, log-rank test, estimand
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CURRENT STATUS: Published
View the published article at BMC Medical Research Methodology.
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Editorial decision: Accept
On 10 Aug, 2020
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On 02 Aug, 2020
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On 01 Aug, 2020
Editor invited
On 01 Aug, 2020
No comments provided
Editorial decision: Minor revision
On 30 Jul, 2020
Review #3 received
Received 29 Jul, 2020
Reviewers invited
Invitations sent on 27 Jul, 2020
Reviewer #1 agreed
On 27 Jul, 2020
Reviewer #2 agreed
On 27 Jul, 2020
Reviewer #3 agreed
On 27 Jul, 2020
Review #1 received
Received 27 Jul, 2020
Review #2 received
Received 27 Jul, 2020
Editor assigned
On 29 Jun, 2020
Submission checks complete
On 28 Jun, 2020
Editor invited
On 28 Jun, 2020
Version 1
Posted 25 Apr, 2020
No comments provided
Editorial decision: Major revision
On 11 Jun, 2020
Review #3 received
Received 11 Jun, 2020
Review #2 received
Received 05 Jun, 2020
Reviewer #2 agreed
On 13 May, 2020
Reviewer #3 agreed
On 13 May, 2020
Review #1 received
Received 05 Apr, 2020
Reviewers invited
Invitations sent on 24 Mar, 2020
Reviewer #1 agreed
On 24 Mar, 2020
Editor assigned
On 16 Mar, 2020
Submission checks complete
On 15 Mar, 2020
Editor invited
On 15 Mar, 2020
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Health Economics & Outcomes Research
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See the published version of this preprint at BMC Medical Research Methodology.
This is a preprint, a preliminary version of a manuscript that has not completed peer review at a journal. Research Square does not conduct peer review prior to posting preprints. The posting of a preprint on this server should not be interpreted as an endorsement of its validity or suitability for dissemination as established information or for guiding clinical practice.
Learn more about In Review
Technical advance
Badges
Prescreen
This manuscript passed our Prescreen assessment
Complete author information
Appropriate declaration statements
Potential risks to human health
Prescreen
Peer Review Timeline
CURRENT STATUS: Published
View the published article at BMC Medical Research Methodology.
No community comments so far
Editorial decision: Accept
On 10 Aug, 2020
Editor assigned
On 02 Aug, 2020
Submission checks complete
On 01 Aug, 2020
Editor invited
On 01 Aug, 2020
No comments provided
Editorial decision: Minor revision
On 30 Jul, 2020
Review #3 received
Received 29 Jul, 2020
Reviewers invited
Invitations sent on 27 Jul, 2020
Reviewer #1 agreed
On 27 Jul, 2020
Reviewer #2 agreed
On 27 Jul, 2020
Reviewer #3 agreed
On 27 Jul, 2020
Review #1 received
Received 27 Jul, 2020
Review #2 received
Received 27 Jul, 2020
Editor assigned
On 29 Jun, 2020
Submission checks complete
On 28 Jun, 2020
Editor invited
On 28 Jun, 2020
Version 1
Posted 25 Apr, 2020
No comments provided
Editorial decision: Major revision
On 11 Jun, 2020
Review #3 received
Received 11 Jun, 2020
Review #2 received
Received 05 Jun, 2020
Reviewer #2 agreed
On 13 May, 2020
Reviewer #3 agreed
On 13 May, 2020
Review #1 received
Received 05 Apr, 2020
Reviewers invited
Invitations sent on 24 Mar, 2020
Reviewer #1 agreed
On 24 Mar, 2020
Editor assigned
On 16 Mar, 2020
Submission checks complete
On 15 Mar, 2020
Editor invited
On 15 Mar, 2020
Metrics
Comments: 0
PDF Downloads: ...
HTML Views: ...
Subject Areas
Health Economics & Outcomes Research
License:
This work is licensed under a CC BY 4.0 License. Read Full License
View the published article at BMC Medical Research Methodology.
Background The data from immuno-oncology therapy trials often show delayed effects, cure rate, crossing hazards, or some mixture of these phenomena. Thus, the common assumption of proportional hazards is often violated such that the commonly used log-rank test can be very underpowered. In these trials, the conventional hazard ratio for describing the treatment effect may not be a good estimand due to the lack of an easily understandable interpretation. To overcome this challenge, restricted mean survival time (RMST) has been strongly recommended for survival analysis in clinical literature due to its independence of the proportional hazard assumption as well as a more clinically meaningful interpretation. The RMST also aligns well with the estimand associated with the analysis from the recommendation in ICH E-9 (R1), and the test/estimation coherency. Currently, the Kaplan Meier (KM) curve is commonly applied to RMST related analyses. Due to some drawbacks of the KM approach such as the limitation in extrapolating to time points beyond the follow-up time, and the large variance at time points with small numbers of events, the RMST may be hindered.
Methods To fully enhance the RMST method, the survival curve using a mixture model is proposed in this paper to construct dynamic RMST curves to evaluate and monitor survival analysis in clinical trials.
Results This new dynamic RMST curve overcomes the drawbacks from the KM approach. The good performance of this new proposal is illustrated through three real examples.
Conclusions RMST provides a clinically meaningful and easily interpretable measure for survival clinical trials. The proposed dynamic RMST approach provides a useful tool for assessing treatment effect over different time frames for survival clinical trials. This dynamic RMST curve can also be useful for checking whether the follow-up time for a study is long enough to demonstrate a treatment difference. The prediction feature of the dynamic RMST analysis may also be used for determining an appropriate time point for an interim analysis , and an evaluation tool for study recommendation from DMC .
Figure 1
Figure 2
Figure 3
Figure 4
Figure 5
Figure 6
The full text of this article is available to read as a PDF.
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