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Geostatistical survival model with Gaussian random effect

Author

Listed:
  • K. Motarjem

    (Tarbiat Modares University)

  • M. Mohammadzadeh

    (Tarbiat Modares University)

  • A. Abyar

    (Tarbiat Modares University)

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.

Suggested Citation

  • K. Motarjem & M. Mohammadzadeh & A. Abyar, 2020. "Geostatistical survival model with Gaussian random effect," Statistical Papers, Springer, vol. 61(1), pages 85-107, February.
    • Handle: RePEc:spr:stpapr:v:61:y:2020:i:1:d:10.1007_s00362-017-0922-8
    DOI: 10.1007/s00362-017-0922-8
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    References listed on IDEAS

    as
    1. Yi Li & Louise Ryan, 2002. "Modeling Spatial Survival Data Using Semiparametric Frailty Models," Biometrics, The International Biometric Society, vol. 58(2), pages 287-297, June.
    2. James Vaupel & Kenneth Manton & Eric Stallard, 1979. "The impact of heterogeneity in individual frailty on the dynamics of mortality," Demography, Springer;Population Association of America (PAA), vol. 16(3), pages 439-454, August.
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