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Cancer Causes Control. 2013 Mar;24(3):505-15. doi: 10.1007/s10552-012-0141-5. Epub 2013 Jan 8.

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

Author information

1
Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Box 281, 171 77 Stockholm, Sweden. sandra.eloranta@ki.se

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.

PMID:
23296456
DOI:
10.1007/s10552-012-0141-5
[Indexed for MEDLINE]

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