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Joint Model for Mortality and Hospitalization

Author

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  • Chen Yuqi

    (Statistics & Applied Probability – University of California – Santa Barbara, Santa Barbara, CA, USA)

  • Guo Wensheng

    (Department of Biostatistics and Epidemiology – Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA)

  • Kotanko Peter

    (Research Division – Renal Research Institute, New York, NY, USA)

  • Usvyat Len

    (Fresenius Medical Care North America, Waltham, MA, USA)

  • Wang Yuedong

    (Statistics & Applied Probability – University of California – Santa Barbara, Santa Barbara, CA, USA)

Abstract

Modeling hospitalization is complicated because the follow-up time can be censored due to death. In this paper, we propose a shared frailty joint model for survival time and hospitalization. A random effect semi-parametric proportional hazard model is assumed for the survival time and conditional on the follow-up time, hospital admissions or total length of stay is modeled by a generalized linear model with a nonparametric offset function of the follow-up time. We assume that the hospitalization and the survival time are correlated through a latent subject-specific random frailty. The proposed model can be implemented using existing software such as SAS Proc NLMIXED. We demonstrate the feasibility through simulations. We apply our methods to study hospital admissions and total length of stay in a cohort of patients on hemodialysis. We identify age, albumin, neutrophil to lymphocyte ratio (NLR) and vintage as significant risk factors for mortality, and age, gender, race, albumin, NLR, pre-dialysis systolic blood pressure (preSBP), interdialytic weight gain (IDWG) and equilibrated Kt/V (eKt/V) as significant risk factors for both hospital admissions and total length of stay. In addition, hospitalization admissions is positively associated with vintage.

Suggested Citation

  • Chen Yuqi & Guo Wensheng & Kotanko Peter & Usvyat Len & Wang Yuedong, 2016. "Joint Model for Mortality and Hospitalization," The International Journal of Biostatistics, De Gruyter, vol. 12(2), pages 1-11, November.
    • Handle: RePEc:bpj:ijbist:v:12:y:2016:i:2:p:11:n:12
    DOI: 10.1515/ijb-2016-0002
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    References listed on IDEAS

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    1. Mary Dupuis Sammel & Louise M. Ryan & Julie M. Legler, 1997. "Latent Variable Models for Mixed Discrete and Continuous Outcomes," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 59(3), pages 667-678.
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