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Predicting Future Excess Events in Risk Assessment

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  • Kyoji Furukawa
  • John B. Cologne
  • Yukiko Shimizu
  • N. Phillip Ross

Abstract

Risk characterization in a study population relies on cases of disease or death that are causally related to the exposure under study. The number of such cases, so‐called “excess” cases, is not just an indicator of the impact of the risk factor in the study population, but also an important determinant of statistical power for assessing aspects of risk such as age‐time trends and susceptible subgroups. In determining how large a population to study and/or how long to follow a study population to accumulate sufficient excess cases, it is necessary to predict future risk. In this study, focusing on models involving excess risk with possible effect modification, we describe a method for predicting the expected magnitude of numbers of excess cases and assess the uncertainty in those predictions. We do this by extending Bayesian APC models for rate projection to include exposure‐related excess risk with possible effect modification by, e.g., age at exposure and attained age. The method is illustrated using the follow‐up study of Japanese Atomic‐Bomb Survivors, one of the primary bases for determining long‐term health effects of radiation exposure and assessment of risk for radiation protection purposes. Using models selected by a predictive‐performance measure obtained on test data reserved for cross‐validation, we project excess counts due to radiation exposure and lifetime risk measures (risk of exposure‐induced deaths (REID) and loss of life expectancy (LLE)) associated with cancer and noncancer disease deaths in the A‐Bomb survivor cohort.

Suggested Citation

  • Kyoji Furukawa & John B. Cologne & Yukiko Shimizu & N. Phillip Ross, 2009. "Predicting Future Excess Events in Risk Assessment," Risk Analysis, John Wiley & Sons, vol. 29(6), pages 885-899, June.
    • Handle: RePEc:wly:riskan:v:29:y:2009:i:6:p:885-899
    DOI: 10.1111/j.1539-6924.2009.01197.x
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    References listed on IDEAS

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    1. Isabelle Bray, 2002. "Application of Markov chain Monte Carlo methods to projecting cancer incidence and mortality," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 51(2), pages 151-164, May.
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    1. Kyoji Furukawa & Munechika Misumi & John B. Cologne & Harry M. Cullings, 2016. "A Bayesian Semiparametric Model for Radiation Dose‐Response Estimation," Risk Analysis, John Wiley & Sons, vol. 36(6), pages 1211-1223, June.

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