Joint Model for Mortality and Hospitalization

Chen Yuqi (), Guo Wensheng (), Kotanko Peter (), Usvyat Len () and Wang Yuedong ()
Additional contact information
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

The International Journal of Biostatistics, 2016, vol. 12, issue 2, 11

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.

Keywords: end stage rental disease; hemodialysis; mixed outcomes; random effect; spline (search for similar items in EconPapers)
Date: 2016
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://doi.org/10.1515/ijb-2016-0002 (text/html)
For access to full text, subscription to the journal or payment for the individual article is required.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:bpj:ijbist:v:12:y:2016:i:2:p:11:n:12

Ordering information: This journal article can be ordered from
https://www.degruyter.com/journal/key/ijb/html

DOI: 10.1515/ijb-2016-0002

Access Statistics for this article

The International Journal of Biostatistics is currently edited by Antoine Chambaz, Alan E. Hubbard and Mark J. van der Laan

More articles in The International Journal of Biostatistics from De Gruyter
Bibliographic data for series maintained by Peter Golla ().