Cumulative cause-specific mortality for cancer patients in the presence ofother causes: a crude analogue of relative survival

Citation
Ka. Cronin et Ej. Feuer, Cumulative cause-specific mortality for cancer patients in the presence ofother causes: a crude analogue of relative survival, STAT MED, 19(13), 2000, pp. 1729-1740
Citations number
21
Categorie Soggetti
Research/Laboratory Medicine & Medical Tecnology","Medical Research General Topics
Journal title
STATISTICS IN MEDICINE
ISSN journal
02776715 → ACNP
Volume
19
Issue
13
Year of publication
2000
Pages
1729 - 1740
Database
ISI
SICI code
0277-6715(20000715)19:13<1729:CCMFCP>2.0.ZU;2-4
Abstract
A common population-based cancer progress measure for net survival (surviva l in the absence of other causes) of cancer patients is relative survival. Relative survival is defined as the ratio of a population of observed survi vors in a cohort of cancer patients to the proportion of expected survivors in a comparable set of cancer-free individuals in the general public, thus giving a measure of excess mortality due to cancer. Relative survival was originally designed to address the question of whether or not there is evid ence that patients have been cured. It has proven to be a useful survival m easure in several areas, including the evaluation of cancer control efforts and the application of cure models. However, it is not representative of t he actual survival patterns observed in a cohort of cancer patients. This p aper suggests a measure for cumulative crude tin the presence of other caus es) cause-specific probability of death for a population diagnosed with can cer. The measure does not use cause of death information which can be unrel iable for population cancer registries. Point estimates and variances are d erived for crude cause-specific probability of death using relative surviva l instead of cause of death information. Examples are given for men diagnos ed with localized prostate cancer over the age of 70 and women diagnosed wi th regional breast cancer using Surveillance, Epidemiology and End Results (SEER) Program data. The examples emphasize the differences in crude and ne t mortality measures and suggest areas where a crude measure is more inform ative. Estimates of this type are especially important for older patients a s new screening modalities detect cancers earlier and choice of treatment o r even 'watchful waiting' become viable options. Published in 2000 by John Wiley & Sons, Ltd.