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

Abstract

The Armitage—Doll model with random frailty can fail to describe incidence rates of rare cancers influenced by an accelerated biological mechanism at some, possibly short, period of life. We propose a new model to account for this influence. Osteosarcoma and Ewing sarcoma are primary bone cancers with characteristic age-incidence patterns that peak in adolescence. We analyze SEER incidence data for whites younger than 40 years diagnosed during the period 1975−2005, with an Armitage—Doll model with compound Poisson frailty. A new model treating the adolescent growth spurt as the accelerated mechanism affecting cancer development is a significant improvement over that model. We also model the incidence rate conditioning on the event of having developed the cancers before the age of 40 years and compare the results with those predicted by the Armitage—Doll model. Our results support existing evidence of an underlying susceptibility for the two cancers among a very small proportion of the population. In addition, the modeling results suggest that susceptible individuals with a rapid growth spurt acquire the cancers sooner than they otherwise would have, if their growth had been slower. The new model is suitable for modeling incidence rates of rare diseases influenced by an accelerated biological mechanism.

Understanding variation in disease risk: the elusive concept of frailty

“…This approach may also be modified in several ways, including taking into account an expanding host tissue during, for example, puberty. 84 An important element is thus to combine models of carcinogenesis with a realistic understanding of individual differences, 6 , 84 , 85 to better understand features of population age-incidence rates.…”

Basic Equations and Computing Procedures for Frailty Modeling of Carcinogenesis: Application to Pancreatic Cancer Data

“…7 As frailty, the gamma-distribution, compound Poisson distribution, power-variance distribution, as well as other distributions, have been utilized. [8][9][10][11][12] Mathematically, the problem of modeling is stated as a best fitting of the hazard rates with observations using methods of regression analysis. The obtained models are useful when the utilized model parameters have apparent biological meaning and the estimated values of these parameters are consistent with the estimates presented by biological means.…”

Pediatric Rhabdomyosarcoma: Epidemiology and Genetic Susceptibility

“…Osteosarcoma and Ewing sarcoma also illustrate this age-related, second peak in incidence. Valberg et al suggested that an accelerated pubertal growth period may play a role in susceptibility to sarcomas during adolescence [ 20 ].…”