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A lean additive frailty model: With an application to clustering of melanoma in Norwegian families.
Brathovde, Mari; Moger, Tron A; Aalen, Odd O; Grotmol, Tom; Veierød, Marit B; Valberg, Morten.
Afiliación
  • Brathovde M; Oslo Centre for Biostatistics and Epidemiology, Oslo University Hospital, Oslo, Norway.
  • Moger TA; Oslo Centre for Biostatistics and Epidemiology, Department of Biostatistics, Institute of Basic Medical Sciences, University of Oslo, Oslo, Norway.
  • Aalen OO; Department of Health Management and Health Economics, Institute of Health and Society, University of Oslo, Oslo, Norway.
  • Grotmol T; Oslo Centre for Biostatistics and Epidemiology, Department of Biostatistics, Institute of Basic Medical Sciences, University of Oslo, Oslo, Norway.
  • Veierød MB; Cancer Registry of Norway, Oslo, Norway.
  • Valberg M; Oslo Centre for Biostatistics and Epidemiology, Department of Biostatistics, Institute of Basic Medical Sciences, University of Oslo, Oslo, Norway.
Stat Med ; 42(23): 4207-4235, 2023 10 15.
Article en En | MEDLINE | ID: mdl-37527835
Additive frailty models are used to model correlated survival data. However, the complexity of the models increases with cluster size to the extent that practical usage becomes increasingly challenging. We present a modification of the additive genetic gamma frailty (AGGF) model, the lean AGGF (L-AGGF) model, which alleviates some of these challenges by using a leaner additive decomposition of the frailty. The performances of the models were compared and evaluated in a simulation study. The L-AGGF model was used to analyze population-wide data on clustering of melanoma in 2 391 125 two-generational Norwegian families, 1960-2015. Using this model, we could analyze the complete data set, while the original model limited the analysis to a restricted data set (with cluster sizes ≤ 7 $$ \le 7 $$ ). We found a substantial clustering of melanoma in Norwegian families and large heterogeneity in melanoma risk across the population, where 52% of the frailty was attributed to the 10% of the population at highest unobserved risk. Due to the improved scalability, the L-AGGF model enables a wider range of analyses of population-wide data compared to the AGGF model. Moreover, the methods outlined here make it possible to perform these analyses in a computationally efficient manner.
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Fragilidad / Melanoma Tipo de estudio: Risk_factors_studies Límite: Humans Idioma: En Revista: Stat Med Año: 2023 Tipo del documento: Article País de afiliación: Noruega Pais de publicación: Reino Unido

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Fragilidad / Melanoma Tipo de estudio: Risk_factors_studies Límite: Humans Idioma: En Revista: Stat Med Año: 2023 Tipo del documento: Article País de afiliación: Noruega Pais de publicación: Reino Unido