A Generalized Beta Model for the Age Distribution of Cancers: Application to Pancreatic and Kidney Cancer

• Main Authors: Tengiz Mdzinarishvili, Michael X. Gleason, Leo Kinarsky, Simon Sherman
• Format: Article
• Language: English
• Published: SAGE Publishing 2009-01-01
• Series: Cancer Informatics
• Subjects: cancer; incidence rates; aging; histopathology
• Online Access: http://www.la-press.com/a-generalized-beta-model-for-the-age-distribution-of-cancers-applicati-a1578

The relationships between cancer incidence rates and the age of patients at cancer diagnosis are a quantitative basis for modeling age distributions of cancer. The obtained model parameters are needed to build rigorous statistical and biological models of cancer development. In this work, a new mathematical model, called the Generalized Beta (GB) model is proposed. Confidence intervals for parameters of this model are derived from a regression analysis. The GB model was used to approximate the incidence rates of the first primary, microscopically confirmed cases of pancreatic cancer (PC) and kidney cancer (KC) that served as a test bed for the proposed approach. The use of the GB model allowed us to determine analytical functions that provide an excellent fit for the observed incidence rates for PC and KC in white males and females. We make the case that the cancer incidence rates can be characterized by a unique set of model parameters (such as an overall cancer rate, and the degree of increase and decrease of cancer incidence rates). Our results suggest that the proposed approach significantly expands possibilities and improves the performance of existing mathematical models and will be very useful for modeling carcinogenic processes characteristic of cancers. To better understand the biological plausibility behind the aforementioned model parameters, detailed molecular, cellular, and tissue-specific mechanisms underlying the development of each type of cancer require further investigation. The model parameters that can be assessed by the proposed approach will complement and challenge future biomedical and epidemiological studies.