Literature DB >> 22744906

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

Morten Valberg1, Tom Grotmol, Steinar Tretli, Marit B Veierød, Susan S Devesa, Odd O Aalen.   

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 Surveillance, Epidemiology and End Result program 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.
Copyright © 2012 John Wiley & Sons, Ltd.

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Year:  2012        PMID: 22744906      PMCID: PMC4052707          DOI: 10.1002/sim.5441

Source DB:  PubMed          Journal:  Stat Med        ISSN: 0277-6715            Impact factor:   2.373


  24 in total

1.  Frailty modeling of the bimodal age-incidence of Hodgkin lymphoma in the Nordic countries.

Authors:  Tom Grotmol; Freddie Bray; Harald Holte; Marion Haugen; Lauren Kunz; Steinar Tretli; Odd O Aalen; Tron A Moger
Journal:  Cancer Epidemiol Biomarkers Prev       Date:  2011-05-10       Impact factor: 4.254

2.  Telomere length and variation in telomere biology genes in individuals with osteosarcoma.

Authors:  Lisa Mirabello; Elliott G Richards; Linh M Duong; Kai Yu; Zhaoming Wang; Richard Cawthon; Sonja I Berndt; Laurie Burdett; Salma Chowdhury; Kedest Teshome; Chester Douglass; Sharon A Savage
Journal:  Int J Mol Epidemiol Genet       Date:  2010-11-23

3.  Height at diagnosis and birth-weight as risk factors for osteosarcoma.

Authors:  Lisa Mirabello; Ruth Pfeiffer; Gwen Murphy; Najat C Daw; Ana Patiño-Garcia; Rebecca J Troisi; Robert N Hoover; Chester Douglass; Joachim Schüz; Alan W Craft; Sharon A Savage
Journal:  Cancer Causes Control       Date:  2011-04-05       Impact factor: 2.506

Review 4.  Epidemiology of bone tumours in children and young adults.

Authors:  Rachel Eyre; Richard G Feltbower; Emmanuel Mubwandarikwa; Tim O B Eden; Richard J Q McNally
Journal:  Pediatr Blood Cancer       Date:  2009-12       Impact factor: 3.167

5.  Mutation and cancer: statistical study of retinoblastoma.

Authors:  A G Knudson
Journal:  Proc Natl Acad Sci U S A       Date:  1971-04       Impact factor: 11.205

Review 6.  Evaluation of data quality in the cancer registry: principles and methods Part II. Completeness.

Authors:  D Max Parkin; Freddie Bray
Journal:  Eur J Cancer       Date:  2009-01-06       Impact factor: 9.162

7.  Frailty modelling of testicular cancer incidence using Scandinavian data.

Authors:  Tron A Moger; Odd O Aalen; Tarje O Halvorsen; Hans H Storm; Steinar Tretli
Journal:  Biostatistics       Date:  2004-01       Impact factor: 5.899

8.  Osteosarcoma incidence and survival rates from 1973 to 2004: data from the Surveillance, Epidemiology, and End Results Program.

Authors:  Lisa Mirabello; Rebecca J Troisi; Sharon A Savage
Journal:  Cancer       Date:  2009-04-01       Impact factor: 6.860

9.  Using epidemiology and genomics to understand osteosarcoma etiology.

Authors:  Sharon A Savage; Lisa Mirabello
Journal:  Sarcoma       Date:  2011-03-08

10.  A comprehensive candidate gene approach identifies genetic variation associated with osteosarcoma.

Authors:  Lisa Mirabello; Kai Yu; Sonja I Berndt; Laurie Burdett; Zhaoming Wang; Salma Chowdhury; Kedest Teshome; Arinze Uzoka; Amy Hutchinson; Tom Grotmol; Chester Douglass; Richard B Hayes; Robert N Hoover; Sharon A Savage
Journal:  BMC Cancer       Date:  2011-05-29       Impact factor: 4.430

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  4 in total

1.  Prostate-specific antigen testing for prostate cancer: Depleting a limited pool of susceptible individuals?

Authors:  Morten Valberg; Tom Grotmol; Steinar Tretli; Marit B Veierød; Tron A Moger; Susan S Devesa; Odd O Aalen
Journal:  Eur J Epidemiol       Date:  2016-07-18       Impact factor: 8.082

Review 2.  Understanding variation in disease risk: the elusive concept of frailty.

Authors:  Odd O Aalen; Morten Valberg; Tom Grotmol; Steinar Tretli
Journal:  Int J Epidemiol       Date:  2014-12-12       Impact factor: 7.196

3.  Basic equations and computing procedures for frailty modeling of carcinogenesis: application to pancreatic cancer data.

Authors:  Tengiz Mdzinarishvili; Simon Sherman
Journal:  Cancer Inform       Date:  2013-02-18

Review 4.  Pediatric Rhabdomyosarcoma: Epidemiology and Genetic Susceptibility.

Authors:  Bailey A Martin-Giacalone; P Adam Weinstein; Sharon E Plon; Philip J Lupo
Journal:  J Clin Med       Date:  2021-05-09       Impact factor: 4.241

  4 in total

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