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Lexis diagram and illness-death model: simulating populations in chronic disease epidemiology.
Brinks, Ralph; Landwehr, Sandra; Fischer-Betz, Rebecca; Schneider, Matthias; Giani, Guido.
Afiliación
  • Brinks R; German Diabetes Center, Institute of Biometry and Epidemiology, Duesseldorf, Germany; University Hospital, Polyclinics for Rheumatology, Duesseldorf, Germany.
  • Landwehr S; German Diabetes Center, Institute of Biometry and Epidemiology, Duesseldorf, Germany; Heinrich-Heine-University, Institute for Statistics in Medicine, Duesseldorf, Germany.
  • Fischer-Betz R; University Hospital, Polyclinics for Rheumatology, Duesseldorf, Germany.
  • Schneider M; University Hospital, Polyclinics for Rheumatology, Duesseldorf, Germany.
  • Giani G; Heinrich-Heine-University, Institute for Statistics in Medicine, Duesseldorf, Germany.
PLoS One ; 9(9): e106043, 2014.
Article en En | MEDLINE | ID: mdl-25215502
Chronic diseases impose a tremendous global health problem of the 21st century. Epidemiological and public health models help to gain insight into the distribution and burden of chronic diseases. Moreover, the models may help to plan appropriate interventions against risk factors. To provide accurate results, models often need to take into account three different time-scales: calendar time, age, and duration since the onset of the disease. Incidence and mortality often change with age and calendar time. In many diseases such as, for example, diabetes and dementia, the mortality of the diseased persons additionally depends on the duration of the disease. The aim of this work is to describe an algorithm and a flexible software framework for the simulation of populations moving in an illness-death model that describes the epidemiology of a chronic disease in the face of the different times-scales. We set up a discrete event simulation in continuous time involving competing risks using the freely available statistical software R. Relevant events are birth, the onset (or diagnosis) of the disease and death with or without the disease. The Lexis diagram keeps track of the different time-scales. Input data are birth rates, incidence and mortality rates, which can be given as numerical values on a grid. The algorithm manages the complex interplay between the rates and the different time-scales. As a result, for each subject in the simulated population, the algorithm provides the calendar time of birth, the age of onset of the disease (if the subject contracts the disease) and the age at death. By this means, the impact of interventions may be estimated and compared.
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Simulación por Computador / Enfermedad Crónica / Muerte / Modelos Teóricos Tipo de estudio: Etiology_studies / Incidence_studies / Observational_studies / Prognostic_studies / Risk_factors_studies / Screening_studies Límite: Female / Humans País/Región como asunto: Europa Idioma: En Revista: PLoS One Asunto de la revista: CIENCIA / MEDICINA Año: 2014 Tipo del documento: Article País de afiliación: Alemania Pais de publicación: Estados Unidos
Texto completo
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Imprimir
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Simulación por Computador / Enfermedad Crónica / Muerte / Modelos Teóricos Tipo de estudio: Etiology_studies / Incidence_studies / Observational_studies / Prognostic_studies / Risk_factors_studies / Screening_studies Límite: Female / Humans País/Región como asunto: Europa Idioma: En Revista: PLoS One Asunto de la revista: CIENCIA / MEDICINA Año: 2014 Tipo del documento: Article País de afiliación: Alemania Pais de publicación: Estados Unidos