Predicting Future Excess Events in Risk Assessment

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.

[1]  M. Plummer.  Penalized loss functions for Bayesian model comparison. , 2008, Biostatistics.

[2]  L Knorr-Held,et al.  Projections of lung cancer mortality in West Germany: a case study in Bayesian prediction. , 2001, Biostatistics.

[3]  D. Pierce,et al.  Age-time patterns of cancer to be anticipated from exposure to general mutagens. , 2003, Biostatistics.

[4]  Bruce K Armstrong,et al.  Lung cancer rate predictions using generalized additive models. , 2005, Biostatistics.

[5]  岩崎 民子.  SOURCES AND EFFECTS OF IONIZING RADIATION : United Nations Scientific Committee on the Effects of Atomic Radiation UNSCEAR 2000 Report to the General Assembly, with Scientific Annexes , 2002 .

[6]  D A Pierce,et al.  Studies of the mortality of atomic bomb survivors. Report 12, part II. Noncancer mortality: 1950-1990. , 1999, Radiation research.

[7]  Daniel O. Stram,et al.  The Errors-in-Variables Problem: Considerations Provided by Radiation Dose-Response Analyses of the A-Bomb Survivor Data , 1992 .

[8]  W. Heidenreich,et al.  Promoting Action of Radiation in the Atomic Bomb Survivor Carcinogenesis Data? , 2007, Radiation research.

[9]  D Clayton,et al.  Models for temporal variation in cancer rates. I: Age-period and age-cohort models. , 1987, Statistics in medicine.

[10]  Charles Mw,et al.  Studies of mortality of atomic bomb survivors. Report 13: Solid cancer and noncancer disease mortality: 1950-1997. , 2003 .

[11]  D. Preston,et al.  Effect of Comparison Group on Inference about Effect Modification by Demographic Factors in Cohort Risk Regression , 2002 .

[12]  D Clayton,et al.  Models for temporal variation in cancer rates. II: Age-period-cohort models. , 1987, Statistics in medicine.

[13]  M. Little,et al.  Flexible dose-response models for Japanese atomic bomb survivor data: Bayesian estimation and prediction of cancer risk , 2004, Radiation and environmental biophysics.

[14]  J. Estève,et al.  Projecting cancer incidence and mortality using Bayesian age-period-cohort models. , 2001, Journal of epidemiology and biostatistics.

[15]  Isabelle Bray,et al.  Application of Markov chain Monte Carlo methods to projecting cancer incidence and mortality , 2002 .

[16]  T. Holford,et al.  An alternative approach to statistical age-period-cohort analysis. , 1985, Journal of chronic diseases.

[17]  M. Little,et al.  Projection of cancer risks from the Japanese atomic bomb survivors to the England and Wales population taking into account uncertainty in risk parameters , 2000, Radiation and environmental biophysics.

[18]  D A Pierce,et al.  Studies of the mortality of atomic bomb survivors. Report 12, Part I. Cancer: 1950-1990. , 1996, Radiation research.

[19]  K. Weiss,et al.  Definition and estimation of lifetime detriment from radiation exposures: principles and methods. , 1992, Health physics.

[20]  D. Clayton,et al.  Statistical Models in Epidemiology , 1993 .

[21]  C Osmond,et al.  Using age, period and cohort models to estimate future mortality rates. , 1985, International journal of epidemiology.

[22]  David J. Spiegelhalter,et al.  Bayesian graphical modelling: a case‐study in monitoring health outcomes , 2002 .

[23]  D Clayton,et al.  Bayesian analysis of survival on multiple time scales. , 1994, Statistics in medicine.

[24]  Yukiko Shimizu,et al.  Studies of Mortality of Atomic Bomb Survivors. Report 13: Solid Cancer and Noncancer Disease Mortality: 1950–1997 , 2003, Radiation research.

[25]  T. Holford.  Understanding the effects of age, period, and cohort on incidence and mortality rates. , 1991, Annual review of public health.

[26]  Sachiyo Funamoto,et al.  Dose Estimation for Atomic Bomb Survivor Studies: Its Evolution and Present Status , 2006, Radiation research.

[27]  Tapabrata Maiti,et al.  Bayesian Data Analysis (2nd ed.) (Book) , 2004 .

[28]  T. Hakulinen,et al.  Precision of incidence predictions based on Poisson distributed observations. , 1994, Statistics in medicine.

[29]  Adrian E. Raftery,et al.  Bayesian model averaging: a tutorial (with comments by M. Clyde, David Draper and E. I. George, and a rejoinder by the authors , 1999 .

[30]  Peter Green,et al.  Markov chain Monte Carlo in Practice , 1996 .

[31]  Dale L Preston,et al.  Longevity of atomic-bomb survivors , 2000, The Lancet.