IDEAS home Printed from https://ideas.repec.org/a/bpj/ijbist/v12y2016i2p11n12.html
   My bibliography  Save this article

Joint Model for Mortality and Hospitalization

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: https://doi.org/10.1515/ijb-2016-0002
    Download Restriction: For access to full text, subscription to the journal or payment for the individual article is required.
    File URL: https://libkey.io/10.1515/ijb-2016-0002?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    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.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Yang Lu, 2019. "Flexible (panel) regression models for bivariate count–continuous data with an insurance application," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 182(4), pages 1503-1521, October.
    2. Emilio Augusto Coelho-Barros & Jorge Alberto Achcar & Josmar Mazucheli, 2010. "Longitudinal Poisson modeling: an application for CD4 counting in HIV-infected patients," Journal of Applied Statistics, Taylor & Francis Journals, vol. 37(5), pages 865-880.
    3. Zhang, Q. & Ip, E.H., 2014. "Variable assessment in latent class models," Computational Statistics & Data Analysis, Elsevier, vol. 77(C), pages 146-156.
    4. Zhenzhen Zhang & Thomas M. Braun & Karen E. Peterson & Howard Hu & Martha M. Téllez-Rojo & Brisa N. Sánchez, 2018. "Extending Tests of Random Effects to Assess for Measurement Invariance in Factor Models," Statistics in Biosciences, Springer;International Chinese Statistical Association, vol. 10(3), pages 634-650, December.
    5. Luo, Chongliang & Liang, Jian & Li, Gen & Wang, Fei & Zhang, Changshui & Dey, Dipak K. & Chen, Kun, 2018. "Leveraging mixed and incomplete outcomes via reduced-rank modeling," Journal of Multivariate Analysis, Elsevier, vol. 167(C), pages 378-394.
    6. Leila Amiri & Mojtaba Khazaei & Mojtaba Ganjali, 2018. "A mixture latent variable model for modeling mixed data in heterogeneous populations and its applications," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 102(1), pages 95-115, January.
    7. Zhang, Xiao & Boscardin, W. John & Belin, Thomas R. & Wan, Xiaohai & He, Yulei & Zhang, Kui, 2015. "A Bayesian method for analyzing combinations of continuous, ordinal, and nominal categorical data with missing values," Journal of Multivariate Analysis, Elsevier, vol. 135(C), pages 43-58.
    8. Samson B. Adebayo & Ludwig Fahrmeir & Christian Seiler & Christian Heumann, 2011. "Geoadditive Latent Variable Modeling of Count Data on Multiple Sexual Partnering in Nigeria," Biometrics, The International Biometric Society, vol. 67(2), pages 620-628, June.
    9. Hoshino, Takahiro, 2008. "A Bayesian propensity score adjustment for latent variable modeling and MCMC algorithm," Computational Statistics & Data Analysis, Elsevier, vol. 52(3), pages 1413-1429, January.
    10. Nussbaum, Frank & Giesen, Joachim, 2020. "Pairwise sparse + low-rank models for variables of mixed type," Journal of Multivariate Analysis, Elsevier, vol. 178(C).
    11. Jane Osburn, 2011. "A Latent Variable Approach to Examining the Effects of HR Policies on the Inter- and Intra-Establishment Wage and Employment Structure: A Study of Two Precision Manufacturing Industries," Working Papers 451, U.S. Bureau of Labor Statistics.
    12. D. J. Bartholomew, 2002. "Discussion on the paper by Fayers and Hand," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 165(2), pages 253-261, June.
    13. Takahiro Hoshino & Hiroshi Kurata & Kazuo Shigemasu, 2006. "A Propensity Score Adjustment for Multiple Group Structural Equation Modeling," Psychometrika, Springer;The Psychometric Society, vol. 71(4), pages 691-712, December.
    14. Jun Zhu & Jens C. Eickhoff & Mark S. Kaiser, 2003. "Modeling the Dependence between Number of Trials and Success Probability in Beta-Binomial–Poisson Mixture Distributions," Biometrics, The International Biometric Society, vol. 59(4), pages 955-961, December.
    15. Mieke Beth Thomeer & Rin Reczek & Lawrence Stacey, 2022. "Childbearing Biographies as a Method to Examine Diversity and Clustering of Childbearing Experiences: A Research Brief," Population Research and Policy Review, Springer;Southern Demographic Association (SDA), vol. 41(4), pages 1405-1415, August.
    16. Jenni Niku & David I. Warton & Francis K. C. Hui & Sara Taskinen, 2017. "Generalized Linear Latent Variable Models for Multivariate Count and Biomass Data in Ecology," Journal of Agricultural, Biological and Environmental Statistics, Springer;The International Biometric Society;American Statistical Association, vol. 22(4), pages 498-522, December.
    17. Peter M. Fayers & David J. Hand, 2002. "Causal variables, indicator variables and measurement scales: an example from quality of life," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 165(2), pages 233-253, June.
    18. Bayerstadler, Andreas & van Dijk, Linda & Winter, Fabian, 2016. "Bayesian multinomial latent variable modeling for fraud and abuse detection in health insurance," Insurance: Mathematics and Economics, Elsevier, vol. 71(C), pages 244-252.
    19. Feng, Xiangnan & Lu, Bin & Song, Xinyuan & Ma, Shuang, 2019. "Financial literacy and household finances: A Bayesian two-part latent variable modeling approach," Journal of Empirical Finance, Elsevier, vol. 51(C), pages 119-137.
    20. Peter Congdon, 2010. "A multiple indicator, multiple cause method for representing social capital with an application to psychological distress," Journal of Geographical Systems, Springer, vol. 12(1), pages 1-23, March.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:bpj:ijbist:v:12:y:2016:i:2:p:11:n:12. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Peter Golla (email available below). General contact details of provider: https://www.degruyter.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.