Joint modeling for longitudinal covariate and binary outcome via h-likelihood

Toshihiro Misumi ()
Additional contact information
Toshihiro Misumi: Yokohama City University School of Medicine

Statistical Methods & Applications, 2022, vol. 31, issue 5, No 6, 1225-1243

Abstract: Abstract Joint modeling techniques of longitudinal covariates and binary outcomes have attracted considerable attention in medical research. The basic strategy for estimating the coefficients of joint models is to define a joint likelihood based on two submodels with shared random effects. Numerical integration, however, is required in the estimation step for the joint likelihood, which is computationally expensive due to the complexity of the assumed submodels. To overcome this issue, we propose a joint modeling procedure using the h-likelihood to avoid numerical integration in the estimation algorithm. We conduct Monte Carlo simulations to investigate the effectiveness of our proposed modeling procedures by evaluating both the accuracy of the parameter estimates and computational time. The accuracy of the proposed procedure is compared to the two-stage modeling and numerical integration approaches. We also validate our proposed modeling procedure by applying it to the analysis of real data.

Keywords: Binary outcome; H-likelihood; Joint model; Longitudinal covariate; Shared random effects (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
http://link.springer.com/10.1007/s10260-022-00631-8 Abstract (text/html)
Access to the full text of the articles in this series is restricted.

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:spr:stmapp:v:31:y:2022:i:5:d:10.1007_s10260-022-00631-8

Ordering information: This journal article can be ordered from
http://www.springer. ... cs/journal/10260/PS2

DOI: 10.1007/s10260-022-00631-8

Access Statistics for this article

Statistical Methods & Applications is currently edited by Tommaso Proietti

More articles in Statistical Methods & Applications from Springer, Società Italiana di Statistica
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().