Incorporating competing risk theory into evaluations of changes in cancer survival: making the most of cause of death and routinely linked sociodemographic data
Abstract Background Relative survival is the most common method used for measuring survival from population-based registries. However, the relative survival concept of ‘survival as far as the cancer is concerned’ can be biased due to differing non-cancer risk of death in the population with cancer (...
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| Format: | Article |
| Language: | English |
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BMC
2020-06-01
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| Series: | BMC Public Health |
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| Online Access: | http://link.springer.com/article/10.1186/s12889-020-09084-8 |
| _version_ | 1819296210365710336 |
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| author | Cameron M. Wright Anna K. Nowak Georgia Halkett Rachael E. Moorin |
| author_facet | Cameron M. Wright Anna K. Nowak Georgia Halkett Rachael E. Moorin |
| author_sort | Cameron M. Wright |
| collection | DOAJ |
| description | Abstract Background Relative survival is the most common method used for measuring survival from population-based registries. However, the relative survival concept of ‘survival as far as the cancer is concerned’ can be biased due to differing non-cancer risk of death in the population with cancer (competing risks). Furthermore, while relative survival can be stratified or standardised, for example by sex or age, adjustment for a broad range of sociodemographic variables potentially influencing survival is not possible. In this paper we propose Fine and Gray competing risks multivariable regression as a method that can assess the probability of death from cancer, incorporating competing risks and adjusting for sociodemographic confounders. Methods We used whole of population, person-level routinely linked Western Australian cancer registry and mortality data for individuals diagnosed from 1983 to 2011 for major cancer types combined, female breast, colorectal, prostate, lung and pancreatic cancers, and grade IV glioma. The probability of death from the index cancer (cancer death) was evaluated using Fine and Gray competing risks regression, adjusting for age, sex, Indigenous status, socio-economic status, accessibility to services, time sub-period and (for all cancers combined) cancer type. Results When comparing diagnoses in 2008–2011 to 1983–1987, we observed substantial decreases in the rate of cancer death for major cancer types combined (N = 192,641, − 31%), female breast (− 37%), prostate (− 76%) and colorectal cancers (− 37%). In contrast, improvements in pancreatic (− 15%) and lung cancers (− 9%), and grade IV glioma (− 24%) were less and the cumulative probability of cancer death for these cancer types remained high. Conclusion Considering the justifiable expectation for confounder adjustment in observational epidemiological studies, standard methods for tracking population-level changes in cancer survival are simplistic. This study demonstrates how competing risks and sociodemographic covariates can be incorporated using readily available software. While cancer has been focused on here, this technique has potential utility in survival analysis for other disease states. |
| first_indexed | 2024-12-24T04:54:29Z |
| format | Article |
| id | doaj.art-85c91da3f6624ebdaa651c5b389601f3 |
| institution | Directory Open Access Journal |
| issn | 1471-2458 |
| language | English |
| last_indexed | 2024-12-24T04:54:29Z |
| publishDate | 2020-06-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Public Health |
| spelling | doaj.art-85c91da3f6624ebdaa651c5b389601f32022-12-21T17:14:26ZengBMCBMC Public Health1471-24582020-06-0120111310.1186/s12889-020-09084-8Incorporating competing risk theory into evaluations of changes in cancer survival: making the most of cause of death and routinely linked sociodemographic dataCameron M. Wright0Anna K. Nowak1Georgia Halkett2Rachael E. Moorin3Health Economics and Data Analytics, Faculty of Health Sciences, School of Public Health, Curtin UniversityDepartment of Medical Oncology, Sir Charles Gairdner HospitalMidwifery and Paramedicine, Faculty of Health Sciences, School of Nursing, Curtin UniversityHealth Economics and Data Analytics, Faculty of Health Sciences, School of Public Health, Curtin UniversityAbstract Background Relative survival is the most common method used for measuring survival from population-based registries. However, the relative survival concept of ‘survival as far as the cancer is concerned’ can be biased due to differing non-cancer risk of death in the population with cancer (competing risks). Furthermore, while relative survival can be stratified or standardised, for example by sex or age, adjustment for a broad range of sociodemographic variables potentially influencing survival is not possible. In this paper we propose Fine and Gray competing risks multivariable regression as a method that can assess the probability of death from cancer, incorporating competing risks and adjusting for sociodemographic confounders. Methods We used whole of population, person-level routinely linked Western Australian cancer registry and mortality data for individuals diagnosed from 1983 to 2011 for major cancer types combined, female breast, colorectal, prostate, lung and pancreatic cancers, and grade IV glioma. The probability of death from the index cancer (cancer death) was evaluated using Fine and Gray competing risks regression, adjusting for age, sex, Indigenous status, socio-economic status, accessibility to services, time sub-period and (for all cancers combined) cancer type. Results When comparing diagnoses in 2008–2011 to 1983–1987, we observed substantial decreases in the rate of cancer death for major cancer types combined (N = 192,641, − 31%), female breast (− 37%), prostate (− 76%) and colorectal cancers (− 37%). In contrast, improvements in pancreatic (− 15%) and lung cancers (− 9%), and grade IV glioma (− 24%) were less and the cumulative probability of cancer death for these cancer types remained high. Conclusion Considering the justifiable expectation for confounder adjustment in observational epidemiological studies, standard methods for tracking population-level changes in cancer survival are simplistic. This study demonstrates how competing risks and sociodemographic covariates can be incorporated using readily available software. While cancer has been focused on here, this technique has potential utility in survival analysis for other disease states.http://link.springer.com/article/10.1186/s12889-020-09084-8Cancer survivalCompeting risksRelative survival |
| spellingShingle | Cameron M. Wright Anna K. Nowak Georgia Halkett Rachael E. Moorin Incorporating competing risk theory into evaluations of changes in cancer survival: making the most of cause of death and routinely linked sociodemographic data BMC Public Health Cancer survival Competing risks Relative survival |
| title | Incorporating competing risk theory into evaluations of changes in cancer survival: making the most of cause of death and routinely linked sociodemographic data |
| title_full | Incorporating competing risk theory into evaluations of changes in cancer survival: making the most of cause of death and routinely linked sociodemographic data |
| title_fullStr | Incorporating competing risk theory into evaluations of changes in cancer survival: making the most of cause of death and routinely linked sociodemographic data |
| title_full_unstemmed | Incorporating competing risk theory into evaluations of changes in cancer survival: making the most of cause of death and routinely linked sociodemographic data |
| title_short | Incorporating competing risk theory into evaluations of changes in cancer survival: making the most of cause of death and routinely linked sociodemographic data |
| title_sort | incorporating competing risk theory into evaluations of changes in cancer survival making the most of cause of death and routinely linked sociodemographic data |
| topic | Cancer survival Competing risks Relative survival |
| url | http://link.springer.com/article/10.1186/s12889-020-09084-8 |
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