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Cancer Epidemiol Biomarkers Prev. 2011 Dec;20(12):2480-6. doi: 10.1158/1055-9965.EPI-11-0697. Epub 2011 Sep 29.

Deriving valid population-based cancer survival estimates in the presence of nonnegligible proportions of cancers notified by death certificates only.

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Division of Clinical Epidemiology and Aging Research, German Cancer Research Center, Im Neuenheimer Feld 581, 69120 Heidelberg, Germany.



Studies of cancer survival by population-based cancer registries are a key component in monitoring progress against cancer. Patients notified by death certificates only (DCO) are commonly excluded from such studies. The validity of this "exclude DCO" approach has been questioned and an alternative "correct for DCO" approach has been proposed.


We assess the validity of both the "exclude DCO" approach and the "correct for DCO" approach using model calculations. We illustrate implications for population-based cancer survival analyses by analyses of 5-year relative survival of cancer patients in Saarland, Germany.


The "exclude DCO" approach provides (too) optimistic survival estimates and the "correct for DCO" approach provides (too) pessimistic survival estimates under plausible assumptions. For example, in case of true survival of 50%, underascertainment of 5% of surviving patients and of 15% of dying patients (yielding a proportion of DCO cases of 7.7%), the two approaches would provide survival rate estimates of 52.8% and 48.8%, respectively. The difference of survival estimates obtained with both approaches increases with incompleteness of registration and the proportion of DCO cases. Trace back of DCO cases shifts survival estimates from the former toward the latter estimate.


In case of nonnegligible DCO proportions, cancer survival studies should not be exclusively based on either the "exclude DCO" or the "correct for DCO" approach. A combination of estimates from both approaches may be useful to delineate a plausibility range for true survival.


Our results may help to enhance validity and comparability of population-based cancer survival estimates.

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