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Background: Epidemiologic research into cancer and subsequent decision making to reduce the cancer burden in the population are dependent on the quality of available data. The more reliable the data, the more confident we can be that the decisions made would have the desired effect in the population. The North American Association of Central Cancer Registries (NAACCR) certifies population-based cancer registries, ensuring uniformity of data quality. An important assessment of registry quality is provided by the index of completeness of cancer case ascertainment. NAACCR currently computes this index assuming that the ratio of cancer incidence rates to cancer mortality rates is constant across geographic areas within cancer site, gender, and race groups. NAACCR does not incorporate the variability of this index into the certification process.Methods: We propose an improved method for calculating this index based on a statistical model developed at the National Cancer Institute to predict expected incidence using demographic and lifestyle data. We calculate the variance of our index using statistical approximation.Results: We use the incidence model to predict the number of new incident cases in each registry area, based on all available registry data. Then we adjust the registry-specific expected numbers for reporting delay and data corrections. The proposed completeness index is the ratio of the observed number to the adjusted prediction for each registry. We calculate the variance of the new index and propose a simple method of incorporating this variability into the certification process.Conclusions: Better modeling reduces the number of registries with unrealistically high completeness indices. We provide a fuller picture of registry performance by incorporating variability into the certification process. [ABSTRACT FROM AUTHOR]