Different survival analysis methods for measuring long-term outcomes of Indigenous and non-Indigenous Australian cancer patients in the presence and absence of competing risks. Issue 1 (December 2017)

• Record Type: Journal Article
• Authors: He, Vincent; Condon, John; Baade, Peter; Zhang, Xiaohua; Zhao, Yuejen
• Is Part Of: Population health metrics. Volume 15:Issue 1(2017)
• Page Start: 1
• Page End: 8
• Publication Date: 2017-12
• Subjects: Survival analysis -- Indigenous Australians -- Competing risks -- Fine-Gray model -- Net survival -- Crude probability of death -- Cancer prognosis -- Life tables -- Smoking -- Cause of death data; Health status indicators -- Periodicals; Population -- Statistics -- Periodicals; Health status indicators -- Measurement; Health status indicators -- Statistical methods; 614.420727
• DOI: 10.1186/s12963-016-0118-9
• Languages: English
• ISSNs: 1478-7954

Abstract

Abstract Background Net survival is the most common measure of cancer prognosis and has been used to study differentials in cancer survival between ethnic or racial population subgroups. However, net survival ignores competing risks of deaths and so provides incomplete prognostic information for cancer patients, and when comparing survival between populations with different all-cause mortality. Another prognosis measure, "crude probability of death", which takes competing risk of death into account, overcomes this limitation. Similar to net survival, it can be calculated using either life tables (using Cronin-Feuer method) or cause of death data (using Fine-Gray method). The aim of this study is two-fold: (1) to compare the multivariable results produced by different survival analysis methods; and (2) to compare the Cronin-Feuer with the Fine-Gray methods, in estimating the cancer and non-cancer death probability of both Indigenous and non-Indigenous cancer patients and the Indigenous cancer disparities. Methods Cancer survival was investigated for 9, 595 people (18.5% Indigenous) diagnosed with cancer in the Northern Territory of Australia between 1991 and 2009. The Cox proportional hazard model along with Poisson and Fine-Gray regression were used in the multivariable analysis. The crude probabilities of cancer and non-cancer methods were estimated in two ways: first, using cause of death data with the Fine-Gray method, and second, using life tables with the Cronin-Feuer method. Results Multivariable regression using the relative survival, cause-specific survival, and competing risk analysis produced similar results. In the presence of competing risks, the Cronin-Feuer method produced similar results to Fine-Gray in the estimation of cancer death probability (higher Indigenous cancer death probabilities for all cancers) and non-cancer death probabilities (higher Indigenous non-cancer death probabilities for all cancers except lung cancer and head and neck cancers). Cronin-Feuer estimated much lower non-cancer death probabilities than Fine-Gray for non-Indigenous patients with head and neck cancers and lung cancers (both smoking-related cancers). Conclusion Despite the limitations of the Cronin-Feuer method, it is a reasonable alternative to the Fine-Gray method for assessing the Indigenous survival differential in the presence of competing risks when valid and reliable subgroup-specific life tables are available and cause of death data are unavailable or unreliable.