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Geostatistical survival model with Gaussian random effect

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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.

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Acknowledgements

The authors are thankful to the referees for their many helpful comments that greatly improved this paper. We also wish to acknowledge for the support from Center of Excellence of Spatial and Spatio-Temporal Data Analysis in Tarbiat Modares University.

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Authors and Affiliations

  1. Department of Statistics, Tarbiat Modares University, Tehran, Iran

    K. Motarjem, M. Mohammadzadeh & A. Abyar

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  1. K. Motarjem
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  2. M. Mohammadzadeh
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  3. A. Abyar
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Correspondence to M. Mohammadzadeh.

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Motarjem, K., Mohammadzadeh, M. & Abyar, A. Geostatistical survival model with Gaussian random effect. Stat Papers 61, 85–107 (2020). https://doi.org/10.1007/s00362-017-0922-8

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  • DOI: https://doi.org/10.1007/s00362-017-0922-8

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