Joint analysis of site-specific cancer risks for the atomic bomb survivors

• Published in: Radiation research 1993-05, Vol.134 (2), p.134-142
• Main Authors: PIERCE, D. A, PRESTON, D. L
• Format: Article
• Language: English
• Subjects: Age Factors; Biological and medical sciences; Biological effects of radiation; Digestive System Neoplasms - epidemiology; Digestive System Neoplasms - etiology; Female; Fundamental and applied biological sciences. Psychology; Humans; Ionizing radiations; Male; Models, Statistical; Neoplasms, Radiation-Induced - epidemiology; Neoplasms, Radiation-Induced - etiology; Nuclear Warfare; Respiratory Tract Neoplasms - epidemiology; Respiratory Tract Neoplasms - etiology; Risk; Sex Factors; Tissues, organs and organisms biophysics
• ISSN: 0033-7587; 1938-5404
• DOI: 10.2307/3578452

Statistical methods are presented for joint analysis of site-specific cancer risks for the atomic bomb survivors. Previous analyses of these data have been made either without regard to cancer type, excluding leukemia, or separately for types or classes of cancers. Clearly, analyses without regard to cancer type are less than satisfactory. The primary advantages of joint, rather than separate, analyses are that: (1) models can be fitted with some parameters common to cancer types and others type-specific; (2) significance tests can be used to compare type-specific risks; and (3) through consideration of more comprehensive models, a clearer understanding may be obtained of the modifying effects of sex, age at exposure, and time since exposure. Joint analysis is straightforward, entailing primarily the incorporation of another factor, cancer type, in the usual cross-tabulation of the data for analysis. The use of these methods is illustrated in an analysis of three classes of cancer studied by the National Research Council's BEIR V Committee: digestive, respiratory, and other solid tumors. Based on this analysis, some criticism is made of the BEIR V preferred models. The proposed methods are applicable to models for either relative or absolute risks, and results using both types of models are given. Although some of the gains from joint analysis are apparent from the results here, it will be important to use these methods with a more suitable choice of cancer classes and for cancer incidence data where the diagnoses are more accurate.