Literature DB >> 23296456

How can we make cancer survival statistics more useful for patients and clinicians: an illustration using localized prostate cancer in Sweden.

Sandra Eloranta1, Jan Adolfsson, Paul C Lambert, Pär Stattin, Olof Akre, Therese M-L Andersson, Paul W Dickman.   

Abstract

PURPOSE: Studies of cancer patient survival typically report relative survival or cause-specific survival using data from patients diagnosed many years in the past. From a risk-communication perspective, such measures are suboptimal for several reasons; their interpretation is not transparent for non-specialists, competing causes of death are ignored and the estimates are unsuitable to predict the outcome of newly diagnosed patients. In this paper, we discuss the relative merits of recently developed alternatives to traditionally reported measures of cancer patient survival.
METHODS: In a relative survival framework, using a period approach, we estimated probabilities of death in the presence of competing risks. To illustrate the methods, we present estimates of survival among 23,353 initially untreated, or hormonally treated men with intermediate- or high-risk localized prostate cancer using Swedish population-based data.
RESULTS: Among all groups of newly diagnosed patients, the probability of dying from prostate cancer, accounting for competing risks, was lower compared to the corresponding estimates where competing risks were ignored. Accounting for competing deaths was particularly important for patients aged more than 70 years at diagnosis in order to avoid overestimating the risk of dying from prostate cancer.
CONCLUSIONS: We argue that period estimates of survival, accounting for competing risks, provide the tools to communicate the actual risk that cancer patients, diagnosed today, face to die from their disease. Such measures should offer a more useful basis for risk communication between patients and clinicians and we advocate their use as means to answer prognostic questions.

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Year:  2013        PMID: 23296456     DOI: 10.1007/s10552-012-0141-5

Source DB:  PubMed          Journal:  Cancer Causes Control        ISSN: 0957-5243            Impact factor:   2.506


  16 in total

1.  Reference-Adjusted Loss in Life Expectancy for Population-Based Cancer Patient Survival Comparisons-with an Application to Colon Cancer in Sweden.

Authors:  Therese M-L Andersson; Mark J Rutherford; Bjørn Møller; Paul C Lambert; Tor Åge Myklebust
Journal:  Cancer Epidemiol Biomarkers Prev       Date:  2022-09-02       Impact factor: 4.090

2.  Crude probability of death for cancer patients by spread of disease in New South Wales, Australia 1985 to 2014.

Authors:  Xue Qin Yu; Paramita Dasgupta; Clare Kahn; Kou Kou; Susanna Cramb; Peter Baade
Journal:  Cancer Med       Date:  2021-05-06       Impact factor: 4.452

3.  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.

Authors:  Vincent Y F He; John R Condon; Peter D Baade; Xiaohua Zhang; Yuejen Zhao
Journal:  Popul Health Metr       Date:  2017-01-17

4.  Use of relative survival to evaluate non-ST-elevation myocardial infarction quality of care and clinical outcomes.

Authors:  Marlous Hall; Oras A Alabas; Tatendashe B Dondo; Tomas Jernberg; Chris P Gale
Journal:  Eur Heart J Qual Care Clin Outcomes       Date:  2015-11-01

5.  Direct modeling of the crude probability of cancer death and the number of life years lost due to cancer without the need of cause of death: a pseudo-observation approach in the relative survival setting.

Authors:  Dimitra-Kleio Kipourou; Maja Pohar Perme; Bernard Rachet; Aurelien Belot
Journal:  Biostatistics       Date:  2022-01-13       Impact factor: 5.899

6.  Cancer-specific survival by stage of bladder cancer and factors collected by Mallorca Cancer Registry associated to survival.

Authors:  J Ripoll; M Ramos; J Montaño; J Pons; A Ameijide; P Franch
Journal:  BMC Cancer       Date:  2021-06-07       Impact factor: 4.430

7.  Prostate cancer, prostate cancer death, and death from other causes, among men with metabolic aberrations.

Authors:  Christel Häggström; Tanja Stocks; Gabriele Nagel; Jonas Manjer; Tone Bjørge; Göran Hallmans; Anders Engeland; Hanno Ulmer; Björn Lindkvist; Randi Selmer; Hans Concin; Steinar Tretli; Håkan Jonsson; Pär Stattin
Journal:  Epidemiology       Date:  2014-11       Impact factor: 4.822

8.  Model Comparison for Breast Cancer Prognosis Based on Clinical Data.

Authors:  Sabri Boughorbel; Rashid Al-Ali; Naser Elkum
Journal:  PLoS One       Date:  2016-01-15       Impact factor: 3.240

9.  The impact of comorbid disease history on all-cause and cancer-specific mortality in myeloid leukemia and myeloma - a Swedish population-based study.

Authors:  Mohammad Mohammadi; Yang Cao; Ingrid Glimelius; Matteo Bottai; Sandra Eloranta; Karin E Smedby
Journal:  BMC Cancer       Date:  2015-11-05       Impact factor: 4.430

10.  Analysing population-based cancer survival - settling the controversies.

Authors:  Maja Pohar Perme; Jacques Estève; Bernard Rachet
Journal:  BMC Cancer       Date:  2016-12-03       Impact factor: 4.430

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