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Research article
Sarwar Islam Mozumder
University of Leicester
Corresponding Author
ORCiD: https://orcid.org/0000-0001-9644-7525
License:
This work is licensed under a CC BY 4.0 License. Read Full License
We present various measures, specifically the expected life-years list due to a cause of death, that can be predicted for a specific covariate pattern to facilitate interpretation in observational studies. These can also be summarised at the population-level using standardisation to obtain marginal measures. The restricted mean survival time (RMST) measure can be obtained in the presence of competing risks using Royston-Parmar flexible parametric survival models (FPMs). Royston-Parmar FPMs can be fitted on either the cause-specific hazards or cumulative incidence scale in the presence of competing risks. An advantage of modelling within this framework for competing risks data is the ease at which other alternative predictions to the (cause-specific or subdistribution) hazard ratio can be obtained. The RMST estimate is one such measure. This has an attractive interpretation, especially when the proportionality assumption is violated. In addition to this, compared to similar measures, fewer assumptions are required and it does not require extrapolation. Furthermore, one can easily obtain the expected number of life-years lost, or gained, due to a particular cause of death, which is a further useful prognostic measure. We describe estimation of RMST after fitting a FPM on either the log-cumulative subdistribution, or cause-specific hazards scale. As an illustration of reporting such measures to facilitate interpretation of a competing risks analysis, models are fitted to English colorectal data.
Keywords
competing risks, restricted mean survival time, restricted mean life time, flexible parametric model, life-years lost, survival analysis
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Peer Review Timeline
CURRENT STATUS: Published
View the published article at BMC Medical Research Methodology.
No community comments so far
Editorial decision: Accept
On 19 Jan, 2021
Review #1 received
Received 18 Jan, 2021
Reviewers invited
Invitations sent on 10 Jan, 2021
Reviewer #1 agreed
On 10 Jan, 2021
Editor assigned
On 22 Dec, 2020
Submission checks complete
On 22 Dec, 2020
Editor invited
On 22 Dec, 2020
Version 2
Posted 15 Oct, 2020
No comments provided
Editorial decision: Major revision
On 08 Nov, 2020
Review #1 received
Received 01 Nov, 2020
Reviewers invited
Invitations sent on 13 Oct, 2020
Reviewer #1 agreed
On 13 Oct, 2020
Editor assigned
On 09 Oct, 2020
Editor invited
On 08 Oct, 2020
Submission checks complete
On 07 Oct, 2020
No comments provided
Editorial decision: Major revision
On 02 Sep, 2020
Review #2 received
Received 27 Aug, 2020
Review #1 received
Received 25 Mar, 2020
Reviewer #2 agreed
On 24 Feb, 2020
Reviewer #1 agreed
On 24 Feb, 2020
Reviewers invited
Invitations sent on 21 Feb, 2020
Editor assigned
On 14 Feb, 2020
Submission checks complete
On 13 Feb, 2020
Editor invited
On 13 Feb, 2020
First submitted
On 12 Feb, 2020
Metrics
Comments: 0
PDF Downloads: ...
HTML Views: ...
Subject Areas
Health Economics & Outcomes Research
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A new preprint design is coming!
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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
Research article
Sarwar Islam Mozumder
University of Leicester
Corresponding Author
ORCiD: https://orcid.org/0000-0001-9644-7525
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 19 Jan, 2021
Review #1 received
Received 18 Jan, 2021
Reviewers invited
Invitations sent on 10 Jan, 2021
Reviewer #1 agreed
On 10 Jan, 2021
Editor assigned
On 22 Dec, 2020
Submission checks complete
On 22 Dec, 2020
Editor invited
On 22 Dec, 2020
Version 2
Posted 15 Oct, 2020
No comments provided
Editorial decision: Major revision
On 08 Nov, 2020
Review #1 received
Received 01 Nov, 2020
Reviewers invited
Invitations sent on 13 Oct, 2020
Reviewer #1 agreed
On 13 Oct, 2020
Editor assigned
On 09 Oct, 2020
Editor invited
On 08 Oct, 2020
Submission checks complete
On 07 Oct, 2020
No comments provided
Editorial decision: Major revision
On 02 Sep, 2020
Review #2 received
Received 27 Aug, 2020
Review #1 received
Received 25 Mar, 2020
Reviewer #2 agreed
On 24 Feb, 2020
Reviewer #1 agreed
On 24 Feb, 2020
Reviewers invited
Invitations sent on 21 Feb, 2020
Editor assigned
On 14 Feb, 2020
Submission checks complete
On 13 Feb, 2020
Editor invited
On 13 Feb, 2020
First submitted
On 12 Feb, 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.
We present various measures, specifically the expected life-years list due to a cause of death, that can be predicted for a specific covariate pattern to facilitate interpretation in observational studies. These can also be summarised at the population-level using standardisation to obtain marginal measures. The restricted mean survival time (RMST) measure can be obtained in the presence of competing risks using Royston-Parmar flexible parametric survival models (FPMs). Royston-Parmar FPMs can be fitted on either the cause-specific hazards or cumulative incidence scale in the presence of competing risks. An advantage of modelling within this framework for competing risks data is the ease at which other alternative predictions to the (cause-specific or subdistribution) hazard ratio can be obtained. The RMST estimate is one such measure. This has an attractive interpretation, especially when the proportionality assumption is violated. In addition to this, compared to similar measures, fewer assumptions are required and it does not require extrapolation. Furthermore, one can easily obtain the expected number of life-years lost, or gained, due to a particular cause of death, which is a further useful prognostic measure. We describe estimation of RMST after fitting a FPM on either the log-cumulative subdistribution, or cause-specific hazards scale. As an illustration of reporting such measures to facilitate interpretation of a competing risks analysis, models are fitted to English colorectal data.
Figure 1
Figure 2
Figure 3
Figure 4
Figure 5
Figure 6
Figure 7
Figure 8
The full text of this article is available to read as a PDF.
This is a list of supplementary files associated with this preprint. Click to download.
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