Improving communication of cancer survival statistics—feasibility of implementing model-based algorithms in routine publications
Background Routine reporting of cancer patient survival is important, both to monitor the effectiveness of health care and to inform about prognosis following a cancer diagnosis. A range of different survival measures exist, each serving different purposes and targeting different audiences. It is im...
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| Published in | British journal of cancer Vol. 129; no. 5; pp. 819 - 828 |
|---|---|
| Main Authors | , , , , , , , , |
| Format | Journal Article |
| Language | English |
| Published |
London
Nature Publishing Group UK
21.09.2023
Nature Publishing Group |
| Subjects | |
| Online Access | Get full text |
| ISSN | 0007-0920 1532-1827 1532-1827 |
| DOI | 10.1038/s41416-023-02360-5 |
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| Abstract | Background
Routine reporting of cancer patient survival is important, both to monitor the effectiveness of health care and to inform about prognosis following a cancer diagnosis. A range of different survival measures exist, each serving different purposes and targeting different audiences. It is important that routine publications expand on current practice and provide estimates on a wider range of survival measures. We examine the feasibility of automated production of such statistics.
Methods
We used data on 23 cancer sites obtained from the Cancer Registry of Norway (CRN). We propose an automated way of estimating flexible parametric relative survival models and calculating estimates of net survival, crude probabilities, and loss in life expectancy across many cancer sites and subgroups of patients.
Results
For 21 of 23 cancer sites, we were able to estimate survival models without assuming proportional hazards. Reliable estimates of all desired measures were obtained for all cancer sites.
Discussion
It may be challenging to implement new survival measures in routine publications as it can require the application of modeling techniques. We propose a way of automating the production of such statistics and show that we can obtain reliable estimates across a range of measures and subgroups of patients. |
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| AbstractList | Background
Routine reporting of cancer patient survival is important, both to monitor the effectiveness of health care and to inform about prognosis following a cancer diagnosis. A range of different survival measures exist, each serving different purposes and targeting different audiences. It is important that routine publications expand on current practice and provide estimates on a wider range of survival measures. We examine the feasibility of automated production of such statistics.
Methods
We used data on 23 cancer sites obtained from the Cancer Registry of Norway (CRN). We propose an automated way of estimating flexible parametric relative survival models and calculating estimates of net survival, crude probabilities, and loss in life expectancy across many cancer sites and subgroups of patients.
Results
For 21 of 23 cancer sites, we were able to estimate survival models without assuming proportional hazards. Reliable estimates of all desired measures were obtained for all cancer sites.
Discussion
It may be challenging to implement new survival measures in routine publications as it can require the application of modeling techniques. We propose a way of automating the production of such statistics and show that we can obtain reliable estimates across a range of measures and subgroups of patients. Routine reporting of cancer patient survival is important, both to monitor the effectiveness of health care and to inform about prognosis following a cancer diagnosis. A range of different survival measures exist, each serving different purposes and targeting different audiences. It is important that routine publications expand on current practice and provide estimates on a wider range of survival measures. We examine the feasibility of automated production of such statistics.BACKGROUNDRoutine reporting of cancer patient survival is important, both to monitor the effectiveness of health care and to inform about prognosis following a cancer diagnosis. A range of different survival measures exist, each serving different purposes and targeting different audiences. It is important that routine publications expand on current practice and provide estimates on a wider range of survival measures. We examine the feasibility of automated production of such statistics.We used data on 23 cancer sites obtained from the Cancer Registry of Norway (CRN). We propose an automated way of estimating flexible parametric relative survival models and calculating estimates of net survival, crude probabilities, and loss in life expectancy across many cancer sites and subgroups of patients.METHODSWe used data on 23 cancer sites obtained from the Cancer Registry of Norway (CRN). We propose an automated way of estimating flexible parametric relative survival models and calculating estimates of net survival, crude probabilities, and loss in life expectancy across many cancer sites and subgroups of patients.For 21 of 23 cancer sites, we were able to estimate survival models without assuming proportional hazards. Reliable estimates of all desired measures were obtained for all cancer sites.RESULTSFor 21 of 23 cancer sites, we were able to estimate survival models without assuming proportional hazards. Reliable estimates of all desired measures were obtained for all cancer sites.It may be challenging to implement new survival measures in routine publications as it can require the application of modeling techniques. We propose a way of automating the production of such statistics and show that we can obtain reliable estimates across a range of measures and subgroups of patients.DISCUSSIONIt may be challenging to implement new survival measures in routine publications as it can require the application of modeling techniques. We propose a way of automating the production of such statistics and show that we can obtain reliable estimates across a range of measures and subgroups of patients. Routine reporting of cancer patient survival is important, both to monitor the effectiveness of health care and to inform about prognosis following a cancer diagnosis. A range of different survival measures exist, each serving different purposes and targeting different audiences. It is important that routine publications expand on current practice and provide estimates on a wider range of survival measures. We examine the feasibility of automated production of such statistics. We used data on 23 cancer sites obtained from the Cancer Registry of Norway (CRN). We propose an automated way of estimating flexible parametric relative survival models and calculating estimates of net survival, crude probabilities, and loss in life expectancy across many cancer sites and subgroups of patients. For 21 of 23 cancer sites, we were able to estimate survival models without assuming proportional hazards. Reliable estimates of all desired measures were obtained for all cancer sites. It may be challenging to implement new survival measures in routine publications as it can require the application of modeling techniques. We propose a way of automating the production of such statistics and show that we can obtain reliable estimates across a range of measures and subgroups of patients. BackgroundRoutine reporting of cancer patient survival is important, both to monitor the effectiveness of health care and to inform about prognosis following a cancer diagnosis. A range of different survival measures exist, each serving different purposes and targeting different audiences. It is important that routine publications expand on current practice and provide estimates on a wider range of survival measures. We examine the feasibility of automated production of such statistics.MethodsWe used data on 23 cancer sites obtained from the Cancer Registry of Norway (CRN). We propose an automated way of estimating flexible parametric relative survival models and calculating estimates of net survival, crude probabilities, and loss in life expectancy across many cancer sites and subgroups of patients.ResultsFor 21 of 23 cancer sites, we were able to estimate survival models without assuming proportional hazards. Reliable estimates of all desired measures were obtained for all cancer sites.DiscussionIt may be challenging to implement new survival measures in routine publications as it can require the application of modeling techniques. We propose a way of automating the production of such statistics and show that we can obtain reliable estimates across a range of measures and subgroups of patients. |
| Author | Møller, Bjørn Lambert, Paul C. Andersson, Therese M. L. Aagnes, Bjarte Rutherford, Mark Myklebust, Tor Åge Dickman, Paul W. Nilssen, Yngvar Johansson, Anna L. V. |
| Author_xml | – sequence: 1 givenname: Tor Åge orcidid: 0000-0003-4645-1635 surname: Myklebust fullname: Myklebust, Tor Åge email: tamy@kreftregisteret.no organization: Department of Registration, Cancer Registry Norway, Department of Research and Innovation, Møre and Romsdal Hospital Trust – sequence: 2 givenname: Bjarte surname: Aagnes fullname: Aagnes, Bjarte organization: Department of Registration, Cancer Registry Norway – sequence: 3 givenname: Yngvar surname: Nilssen fullname: Nilssen, Yngvar organization: Department of Registration, Cancer Registry Norway – sequence: 4 givenname: Mark orcidid: 0000-0003-1557-6697 surname: Rutherford fullname: Rutherford, Mark organization: Biostatistics Research Group, Department of Health Sciences, University of Leicester, International Agency for Research on Cancer – sequence: 5 givenname: Paul C. surname: Lambert fullname: Lambert, Paul C. organization: Biostatistics Research Group, Department of Health Sciences, University of Leicester, Department of Medical Epidemiology and Biostatistics, Karolinska Institutet – sequence: 6 givenname: Therese M. L. orcidid: 0000-0001-8644-9041 surname: Andersson fullname: Andersson, Therese M. L. organization: Department of Medical Epidemiology and Biostatistics, Karolinska Institutet – sequence: 7 givenname: Anna L. V. surname: Johansson fullname: Johansson, Anna L. V. organization: Department of Registration, Cancer Registry Norway, Department of Medical Epidemiology and Biostatistics, Karolinska Institutet – sequence: 8 givenname: Paul W. surname: Dickman fullname: Dickman, Paul W. organization: Department of Medical Epidemiology and Biostatistics, Karolinska Institutet – sequence: 9 givenname: Bjørn surname: Møller fullname: Møller, Bjørn organization: Department of Registration, Cancer Registry Norway |
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