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Modeling Clustered Survival Times of Loblolly Pine with Time-dependent Covariates and Shared Frailties

Author

Listed:
  • Ram Thapa

    (Virginia Tech)

  • Harold E. Burkhart

    (Virginia Tech)

  • Jie Li

    (Virginia Tech)

  • Yili Hong

    (Virginia Tech)

Abstract

Tree mortality is an important component of forest tree and stand growth models, which provide decision support for forest managers. Mortality patterns, however, are highly variable and difficult to describe. Despite numerous investigations aimed at developing tree survival models, there are still important gaps that need to be filled. This paper used a large-scale repeated measure dataset collected from permanent sample plots established in 1980/81 across the natural range of loblolly pine (Pinus taeda L.) in the Piedmont, Atlantic Coastal Plain and Gulf Coastal Plain physiographic regions of the US. The primary objective of this study was to explain the survival of loblolly pine trees using time-varying covariates such as diameter at breast height, total tree height, crown ratio, stand age, stand basal area, and dominant height. In this paper, individual-tree mortality was described using a semiparametric proportional hazards regression model. Shared frailty models were used to account for unobserved heterogeneity not explained by the observed covariates. Our investigation involved developing a modeling comparison procedure, predicting mortality based on a frailty model, and quantifying the predictive ability for tree mortality. The survival model developed using a large scale database provides further understanding of mortality trends in planted stands of loblolly pine. The survival model will enable forest managers to more accurately specify initial planting density, thinning schedules, and other management interventions.

Suggested Citation

  • Ram Thapa & Harold E. Burkhart & Jie Li & Yili Hong, 2016. "Modeling Clustered Survival Times of Loblolly Pine with Time-dependent Covariates and Shared Frailties," Journal of Agricultural, Biological and Environmental Statistics, Springer;The International Biometric Society;American Statistical Association, vol. 21(1), pages 92-110, March.
    • Handle: RePEc:spr:jagbes:v:21:y:2016:i:1:d:10.1007_s13253-015-0217-2
    DOI: 10.1007/s13253-015-0217-2
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    References listed on IDEAS

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    1. Guo X. & Carlin B.P., 2004. "Separate and Joint Modeling of Longitudinal and Event Time Data Using Standard Computer Packages," The American Statistician, American Statistical Association, vol. 58, pages 16-24, February.
    2. Liang Li & Bo Hu & Tom Greene, 2009. "A Semiparametric Joint Model for Longitudinal and Survival Data with Application to Hemodialysis Study," Biometrics, The International Biometric Society, vol. 65(3), pages 737-745, September.
    3. Michael J. Crowther & Keith R. Abrams & Paul C. Lambert, 2013. "Joint modeling of longitudinal and survival data," Stata Journal, StataCorp LP, vol. 13(1), pages 165-184, March.
    4. Gerda Claeskens & Rosemary Nguti & Paul Janssen, 2008. "One-sided tests in shared frailty models," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 17(1), pages 69-82, May.
    5. 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.
    6. Jie Li & Yili Hong & Ram Thapa & Harold E. Burkhart, 2015. "Survival Analysis of Loblolly Pine Trees With Spatially Correlated Random Effects," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 110(510), pages 486-502, June.
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