Poisson excess relative risk modelsnew implementations and software

  1. Manuel Higueras 1
  2. Adam Howes 2
  1. 1 Departamento de Matem ́ aticas y Computación, Universidad de La Rioja
  2. 2 Basque Center for Applied Mathematics
    info

    Basque Center for Applied Mathematics

    Bilbao, España

    ROR 03b21sh32

Revista:
Sort: Statistics and Operations Research Transactions

ISSN: 1696-2281

Año de publicación: 2018

Volumen: 42

Número: 2

Páginas: 237-252

Tipo: Artículo

DOI: 10.2436/20.8080.02.76 WoS: WOS:000454146400006 DIALNET GOOGLE SCHOLAR lock_openAcceso abierto editor

Otras publicaciones en: Sort: Statistics and Operations Research Transactions

Resumen

Two new implementations for fitting Poisson excess relative risk methods are proposed for as- sumed simple models. This allows for estimation of the excess relative risk associated with a unique exposure, where the background risk is modelled by a unique categorical variable, for example gender or attained age levels. Additionally, it is shown how to fit general Poisson linear relative risk models in R. Both simple methods and the R fitting are illustrated in three examples. The first two examples are from the radiation epidemiology literature. Data in the third example are randomly generated with the purpose of sharing it jointly with the R scripts.

€ Ver financiación

Información de financiación

This research was supported by the Basque Government through the BERC 360 2014-2017 and the Spanish Ministry of Economy and Competitiveness MINECO and FEDER: BCAM Severo Ochoa excellence accreditation SEV-2013-0323 and MINECO Challenges MTM2017-82379-R.

Financiadores

Referencias bibliográficas

  • Committee to Assess Health Risks from Exposure to Low Levels of Ionizing Radiation (2006). Health Risks from exposure to low levels of ionizing radiation. BEIR VII Phase 2. Washington: The National Academies Press.
  • Christensen, R., Johnson, W., Brasncum, A. and Hanson, T.E. (2011). Bayesian Ideas and Data Analysis: An Introduction for Scientists and Statisticians. Boca Raton: Champman & Hall/CRC Press.
  • Grant, E.J., Brenner, A., Sugiyama, H., Sakata, R., Sadakane, A., Utada, M., Cahoon, E. K., Milder, C. M., Soda, M., Cullings, H. M., Preston, D. L., Mabuchi, K. and Ozasa, K. (2017). Solid Cancer Incidence among the Life Span Study of Atomic Bomb Survivors: 1958–2009. Radiation Research, 187(5), 513–537.
  • Harbron, R.W., Chapple, C.-L., O’Sullivan, J.J., Lee, C, McHugh, K., Higueras, M. and Pearce, M.S. (2018). Cancer incidence among children and young adults who have undergone x-ray guided cardiac catheterization procedures. European Journal of Epidemiology, 33(4), 393–401.
  • Henningsen, A. and Toomet, O. (2011). maxLik: A package for maximum likelihood estimation in R. Computational Statistics, 26(3), 443–458.
  • Journy, N., Rehel, J.-L., Ducou Le Pointe, H., Lee, C., Brisse, H., Chateil, J.-F., Caer-Lorho, S., Laurier, D. and Bernier, M.-O. (2015). Are the studies on cancer risk from CT scans biased by indication? Elements of answer from a large-scale cohort study in France. British Journal of Cancer, 112(1), 185–193.
  • McCullagh, P. and Nelder, J. (1989). Generalized Linear Models, 2nd edition. Boca Raton: Champman & Hall/CRC Press.
  • Morinña, D. (2016). linERR: Linear Excess Relative Risk Model, version 1.0, URL: https://CRAN.Rproject.org/package=linERR.
  • Pearce, M.S., Salotti, J.A., Little, M.P., Mchugh, K., Lee, C., Kim, K.P., Howe, N.L., Ronckers, C.M., Rajaraman, P., Craft, A.W., Parker, L. and Berrington de González, A. (2012). Radiation exposure from CT scans in childhood and subsequent risk of leukaemia and brain tumours: a retrospective cohort study. Lancet, 380(9840), 499–505.
  • Plummer, M. (2003). JAGS: A program for analysis of Bayesian graphical models using Gibbs sampling. Preston, D.L., Lubin, J.H., Pierce, D.A. and McConney, M.E. (1993). Epicure: user’s guide. Seattle:
  • Hirosoft International Corporation. R Core Team (2017). R: A language and environment for statistical computing. Vienna: R Foundation for
  • Statistical Computing. URL https://www.R-project.org/. Richardson, D.B. (2008). A simple approach for fitting linear relative rate models in SAS.American Journal of Epidemiology, 168(11), 1333–1338.
  • Therneau, T. (2015). A Package for Survival Analysis in S, version 2.38, URL: https://CRAN.R-project.org/ package=survival.
  • Turner, H. and Firth, D. (2018). Generalized nonlinear models in R: An overview of the gnm package, version 1.1-0, URL: https://cran.r-project.org/package=gnm.
Fuente de los datos: Dialnet