1. Bayesian Estimation of Confidence Intervals for NAACCR Predicted Age-adjusted Incidence Rates Gentry White, J. Jackson-Thompson, Missouri Cancer Registry, University of Missouri-Columbia; M.J. King, Missouri Department of Health and Senior Services Abstract: The NAACCR methodology for estimating gender-race-site specific expected age-adjusted incidence rates defines completeness in terms of how close the observed estimate based on the registry data is to the point estimate produced by the NAACCR methodology. While this provides an accepted and easily understood method for estimating the expected age-adjusted rates, it does not take into account the uncertainty associated with the constituent (i.e., both age-specific and age-adjusted) rate estimates. The NAACCR methodology uses the ratio of the age-adjusted SEER incidence rates and the age-adjusted U.S. mortality rates multiplied by the state’s age-adjusted mortality rates to provide a point estimate of the desired age-adjusted incidence rate. Using Bayesian methodology to estimate the probability distributions of NAACCR incidence rates, the resulting empirical distribution can be used to calculate prediction intervals and test hypotheses concerning the expected rate. Considering the variability inherent in these point estimates, the results of the NAACCR method for assessing completeness can be shown in some cases to be ambiguous. Here the MCR estimate clearly exceeds the NAACCR Gold standard and also falls outside the credible interval of the estimated density. The MCR estimate again exceeds the NAACCR Gold standard and the credible interval of the estimated density Note the Gold standard falls below the credible interval. Both the MCR estimate and the Gold Standard are within the credible interval of the estimated density though the MCR estimate is below the Gold standard. For each age group, the incidence rate per 100,000 is said to follow a Poisson distribution. In a Bayesian context, the posterior distribution of the parameter of the Poisson distribution is a Gamma distribution. In order to estimate from the posterior of the age-adjusted rate, the posterior distribution for each age group is sampled and the weighted sum of the samples is then a sample from the age-adjusted rate. This is done for SEER incidence, U.S. mortality and Missouri mortality age-adjusted rates. The ratio and the product of these respective samples is then a sample from the predicted Missouri incidence rate. In this case, 10,000 iterations are performed and the resulting estimated densities and 95% credible intervals are shown. This project was supported in part by a cooperative agreement between the Centers for Disease Control and Prevention (CDC) and the Missouri Department of Health and Senior Services (DHSS) (#U55/CCU721904-04) and a Surveillance Contract between DHSS and the University of Missouri. Data Source: MICA (Missouri Information for Community Assessment) http://www.dhss.mo.gov/mica / Conclusions: Comparing the Bayesian credible intervals to the existing NAACCR point estimates shows agreement in some cases and not in others. While the viability of the use of Bayesian credible interval is open to discussion, the methodology is well-grounded. Some method accounting for the variability in the NAACCR point estimates should be considered.