Literature DB >> 14744824

Frailty modelling of testicular cancer incidence using Scandinavian data.

Tron A Moger1, Odd O Aalen, Tarje O Halvorsen, Hans H Storm, Steinar Tretli.   

Abstract

The incidence of testicular cancer is highest among young men, and then decreases sharply with age. This points towards a frailty effect, where some men have a much greater risk of testicular cancer than the majority of the male population. Those with the highest risk get cancer, drain the group of individuals at risk, and leave a healthy male population which has approximately zero risk of testicular cancer. This leads to the observed decrease in incidence. We discuss a frailty model, where the frailty is compound-Poisson-distributed. This allows for a non-susceptible group (of zero frailty). The model is successfully applied to incidence data from the Danish and Norwegian registries. It is indicated that there was a decrease in incidence for males born during World War II in both countries. Bootstrap analysis is used to find the degree of variation in the estimates. In the Armitage-Doll multistage model, the estimated number of transitions needed for a cell to become malignant is close to 3 for non-seminomas and 4 for seminomas in both the Danish and Norwegian data. This paper demonstrates that a model including a frailty effect fits the incidence data well and gives interesting results and interpretations, although this is no proof of the effect's truth.

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Year:  2004        PMID: 14744824     DOI: 10.1093/biostatistics/5.1.1

Source DB:  PubMed          Journal:  Biostatistics        ISSN: 1465-4644            Impact factor:   5.899


  14 in total

1.  Frailty modelling of colorectal cancer incidence in Norway: indications that individual heterogeneity in risk is related to birth cohort.

Authors:  Elisabeth Svensson; Tron A Moger; Steinar Tretli; Odd O Aalen; Tom Grotmol
Journal:  Eur J Epidemiol       Date:  2006-09-20       Impact factor: 8.082

2.  A new long-term survival model with dispersion induced by discrete frailty.

Authors:  Vicente G Cancho; Márcia A C Macera; Adriano K Suzuki; Francisco Louzada; Katherine E C Zavaleta
Journal:  Lifetime Data Anal       Date:  2019-04-09       Impact factor: 1.588

3.  Factors affecting the decline in incidence of diabetes in the Diabetes Prevention Program Outcomes Study (DPPOS).

Authors:  Richard F Hamman; Edward Horton; Elizabeth Barrett-Connor; George A Bray; Costas A Christophi; Jill Crandall; Jose C Florez; Sarah Fowler; Ronald Goldberg; Steven E Kahn; William C Knowler; John M Lachin; Mary Beth Murphy; Elizabeth Venditti
Journal:  Diabetes       Date:  2014-10-02       Impact factor: 9.461

4.  Estimating effectiveness in HIV prevention trials with a Bayesian hierarchical compound Poisson frailty model.

Authors:  Rebecca Yates Coley; Elizabeth R Brown
Journal:  Stat Med       Date:  2016-02-11       Impact factor: 2.373

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

Authors:  Morten Valberg; Tom Grotmol; Steinar Tretli; Marit B Veierød; Susan S Devesa; Odd O Aalen
Journal:  Stat Med       Date:  2012-06-29       Impact factor: 2.373

6.  A bivariate survival model with compound Poisson frailty.

Authors:  A Wienke; S Ripatti; J Palmgren; A Yashin
Journal:  Stat Med       Date:  2010-01-30       Impact factor: 2.373

7.  Significant calendar period deviations in testicular germ cell tumors indicate that postnatal exposures are etiologically relevant.

Authors:  Crystal Speaks; Katherine A McGlynn; Michael B Cook
Journal:  Cancer Causes Control       Date:  2012-07-28       Impact factor: 2.506

8.  Age-specific incidence data indicate four mutations are required for human testicular cancers.

Authors:  James P Brody
Journal:  PLoS One       Date:  2011-10-06       Impact factor: 3.240

9.  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

10.  Heterogeneity in multistage carcinogenesis and mixture modeling.

Authors:  Sandro Gsteiger; Stephan Morgenthaler
Journal:  Theor Biol Med Model       Date:  2008-07-21       Impact factor: 2.432

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