Tom Grotmol 1 , Freddie Bray , Harald Holte , Marion Haugen , Lauren Kunz , Steinar Tretli , Odd O Aalen , Tron A Moger . Show Affiliations »
Abstract
BACKGROUND: The bimodality of the age-incidence curve of Hodgkin lymphoma (HL) has been ascribed to the existence of subgroups with distinct etiologies. Frailty models can be usefully applied to age-incidence curves of cancer to aid the understanding of biological phenomena in these instances. The models imply that for a given disease, a minority of individuals are at high risk, compared with the low-risk majority. METHODS: Frailty modeling is applied to interpret HL incidence on the basis of population-based cancer registry data from the five Nordic countries for the period 1993 to 2007. There were a total of 8,045 incident cases and 362,843,875 person-years at risk in the study period. RESULTS: A bimodal frailty analysis provides a reasonable fit to the age-incidence curves, employing 2 prototype models, which differ by having the sex covariate included in the frailty component (model 1) or in the baseline Weibull hazard (model 2). Model 2 seemed to fit better with our current understanding of HL than model 1 for the male-to-female ratio, number of rate-limiting steps in the carcinogenic process, and proportion of susceptibles; whereas model 1 performed better related to the heterogeneity in HL among elderly males. CONCLUSION: The present analysis shows that HL age-incidence data are consistent with a bimodal frailty model, indicating that heterogeneity in cancer susceptibility may give rise to bimodality at the population level, although the individual risk remains simple and monotonically increasing by age. IMPACT: Frailty modeling adds to the existing body of knowledge on the heterogeneity in risk of acquiring HL. ©2011 AACR
BACKGROUND: The bimodality of the age-incidence curve of Hodgkin lymphoma (HL) has been ascribed to the existence of subgroups with distinct etiologies. Frailty models can be usefully applied to age-incidence curves of cancer to aid the understanding of biological phenomena in these instances. The models imply that for a given disease, a minority of individuals are at high risk, compared with the low-risk majority. METHODS: Frailty modeling is applied to interpret HL incidence on the basis of population-based cancer registry data from the five Nordic countries for the period 1993 to 2007. There were a total of 8,045 incident cases and 362,843,875 person -years at risk in the study period. RESULTS: A bimodal frailty analysis provides a reasonable fit to the age-incidence curves, employing 2 prototype models, which differ by having the sex covariate included in the frailty component (model 1) or in the baseline Weibull hazard (model 2). Model 2 seemed to fit better with our current understanding of HL than model 1 for the male-to-female ratio, number of rate-limiting steps in the carcinogenic process , and proportion of susceptibles; whereas model 1 performed better related to the heterogeneity in HL among elderly males. CONCLUSION: The present analysis shows that HL age-incidence data are consistent with a bimodal frailty model, indicating that heterogeneity in cancer susceptibility may give rise to bimodality at the population level, although the individual risk remains simple and monotonically increasing by age. IMPACT: Frailty modeling adds to the existing body of knowledge on the heterogeneity in risk of acquiring HL. ©2011 AACR
Entities: Disease
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Year: 2011
PMID: 21558495 DOI: 10.1158/1055-9965.EPI-10-1014
Source DB: PubMed Journal: Cancer Epidemiol Biomarkers Prev ISSN: 1055-9965 Impact factor: 4.254