Geostatistical survival model with Gaussian random effect

K. Motarjem, M. Mohammadzadeh () and A. Abyar
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K. Motarjem: Tarbiat Modares University
M. Mohammadzadeh: Tarbiat Modares University
A. Abyar: Tarbiat Modares University

Statistical Papers, 2020, vol. 61, issue 1, No 5, 85-107

Abstract: Abstract Survival data analysis occasionally encounters a situation that some unknown risk factors affect survival times. One way of considering these factors is the use of frailty models. In some applications, the survival data are spatially correlated. In this paper, a geostatistical spatial survival model is introduced to analyze the survival data where their locations are continuously patterned in a region. Regarding this concern, a simulation method is introduced to generate a set of spatial survival data. Then the efficiency of Cox proportional hazards, frailty and spatial survival models for fitting to spatial survival data are compared. Finally, these models are used to explore the pattern of infecting Cercosporiose in olive trees. Results show that the location of each olive tree can be effective on suffering from Cercosporiose.

Keywords: Cox proportional hazards model; Unknown risk factors; Spatial random effect; Geostatistical spatial survival model; Cercosporiose; 62H11; 62N01; 91B72 (search for similar items in EconPapers)
Date: 2020
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Persistent link: https://EconPapers.repec.org/RePEc:spr:stpapr:v:61:y:2020:i:1:d:10.1007_s00362-017-0922-8

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DOI: 10.1007/s00362-017-0922-8

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