Frailty Modeling of the Bimodal Age–Incidence of Hodgkin Lymphoma in the Nordic Countries

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. Cancer Epidemiol Biomarkers Prev; 20(7); 1350-7. Ó2011 AACR.

Antibody–Drug Conjugates in Relapsed/Refractory CD30-positive Lymphomas

“…The incidence of HL shows a bimodal age distribution, with the first peak in incidence rates in young adults, and the second peak in older people. 5 It is the most common malignancy in adolescents. 6 Despite the fact that first-line combined chemotherapy regimens, including doxorubicin (Adriamycin), bleomycin, vinblastine and dacarbazine (ABVD), achieve durable remission rates in approximately 80 % of cases, 7 a substantial proportion of patients with advanced HL (20-30 %) are refractory to therapy or relapse after initial treatment.…”

The Purpose of Data Analysis Is to Enable Data Reanalysis

“…The National Cancer Institute's Surveillance Epidemiology End Results, public domain [44]. genetic alterations in the two age sets, or two different types of lymphomas that were mistakenly classified under one name, or two separable genetic populations [45], or some flaw in the data (e.g., misdiagnoses, mix-ups during data collection). Explaining the causes for data incongruities is always a scientific challenge.…”

Frailty modeling of age–incidence curves of osteosarcoma and Ewing sarcoma among individuals younger than 40 years

“…Frailty models have been extensively studied in the field of survival analysis [18,[22][23][24]. These models have successfully been used to model peaking cancer incidence rates [2][3][4][5] and are becoming increasingly popular in, for example, cancer epidemiology, exemplified by recent applications in breast cancer research [6,7]. It is thus becoming increasingly important to understand the limitations of these models when analyzing real data.…”

“…Armitage and Doll introduced their classical multistage model of carcinogenesis in 1954 [1]. Later, the Armitage-Doll model with random frailty has become increasingly popular in modeling the incidence rates of various cancers [2][3][4][5][6][7]. In this paper, we describe a problem encountered by such models when the disease in question is very rare and is influenced by some accelerated biological mechanism during a limited time period.…”