DOI: https://doi.org/10.14710/medstat.18.1.73-82

ANALYSIS MULTILEVEL SURVIVAL DATA USING COVARIATE-ADJUSTED FRAILTY PROPORTIONAL HAZARDS MODEL

Krismona Sandelvia  -  Department of Mathematics, Gadjah Mada University, Yogyakarta, Indonesia, Indonesia
*Adhitya Ronnie Effendie orcid scopus  -  Department of Mathematics, Gadjah Mada University, Yogyakarta, Indonesia, Indonesia
Open Access Copyright (c) 2025 MEDIA STATISTIKA under http://creativecommons.org/licenses/by-nc-sa/4.0.

Citation Format:
Abstract
Multilevel survival data is time-to-event data with a hierarchical or nested structure. This study aims to model the data using the Covariate-Adjusted Frailty Proportional Hazards method, which is an extension of the Cox proportional hazards model with the addition of random effects (frailty). Parameter estimation is performed using a Bayesian approach via Markov Chain Monte Carlo (MCMC). This method is applied to analyze repeated observations of Chronic Granulomatous Disease (CGD) infections, with frailty represented by the hospital and the patient. The results of the data analysis indicate that both hospital and patient frailty significantly influence the time to infection, with patient frailty having a greater effect. Additionally, the treatment variable rINF-g significantly in reducing the risk of serious infection for CGD patients by 64.44%.

Note: This article has supplementary file(s).

Fulltext View|Download |  English Version of The Article
ANALYSIS MULTILEVEL SURVIVAL DATA USING COVARIATE-ADJUSTED FRAILTY PROPORTIONAL HAZARDS MODEL
Subject Revision
Type English Version of The Article
  Download (141KB)    Indexing metadata
Keywords: Multilevel Survival; Frailty; Covariate-Adjusted Proportional Hazards; Bayesian MCMC

Article Metrics:

Article Info
Section: Articles
Language : EN
Recent articles
EVALUATING RANDOM FOREST AND XGBOOST FOR BANK CUSTOMER CHURN PREDICTION ON IMBALANCED DATA USING SMOTE AND SMOTE-ENN COMPARISON OF RANDOM FOREST AND SUPPORT VECTOR MACHINE CLASSIFICATION METHODS FOR PREDICTING THE ACCURACY LEVEL OF MADRASAH DATA RANDOM EFFECTS META-REGRESSION USING WEIGHTED LEAST SQUARES (CASE STUDY: EFFECTIVENESS OF ACCEPTANCE AND COMMITMENT THERAPY IN REDUCING DEPRESSION) More recent articles
Most cited articles
Front-Matter DISTRIBUSI RAYLEIGH UNTUK KLAIM AGREGASI ANALISIS KLASIFIKASI KABUPATEN DI JAWA TENGAH BERDASARKAN POPULASI TERNAK MENGGUNAKAN FUZZY CLUSTER MEANS ANALISIS DATA INFLASI DI INDONESIA PASCA KENAIKAN TDL DAN BBM TAHUN 2013 MENGGUNAKAN MODEL REGRESI KERNEL Perbandingan Model Estimasi Artificial Neural Network Optimasi Genetic Algorithm dan Regresi Linier Berganda More cited articles
  1. Berger, J. O. (1985). Manual for SOA Exam MLC, Fall 2009 Edition. New Hartford: SRBooks, Inc
  2. Brooks, S. P., & Gelman, A. (1998). General Methods for Monitoring Convergence of Iterative Simulations. Journal of Computational and Graphical Statistics, 7(4), 434–455
  3. Carroll, K. J. (2003). On The Use and Utility of the Weibull Model in the Analysis of Survival Data. Control Clin Trials, 24(6), 682–701
  4. Diego, I. G., Yolanda, M. G., John, L. S., Osvaldo, V., & Marcelo, B. (2025). The Multivariate Shared Truncated Normal Frailty Model with Application to Medical Data. Scientific Report, 15:30099
  5. Gelman, A., Carlin, J. B., Stern, H. S., Dunson, D. B., Vehtari, A., & Rubin, D. B. (2013). Bayesian Data Analysis (3rd ed.). CRC press
  6. Hox, J. (2002). Multilevel Analysis Techniques and Applications. New Jersey: Lawrence Erlbaum Associates, Inc
  7. Kiprotich, G., Gallardo, D. I., Ramos, P. L., & Augustin, T. (2025). A Shared Frailty Regression Model for Clustered Survival Data. Statistical Methods in Medical Research, 34(7):1385-1412
  8. Kleinbaum, D. G., Klein, M. (2005). Survival Analysis A Self-Learning Text 2nd Edition. London: Springer
  9. Lee, K., Kim, S., & Park, H. (2025). Neural Network-Based Frailty Models for Complex Correlated Outcomes. Journal of Data Science, 0(0): 1-14
  10. Liu, D., Kalbfeisch, J. D., & Schaubel, D. E. (2011). A Positive Stable Frailty Model for Clustered Failure Time Data with Covariate-Dependent Frailty. Biometrics, 67(1): 8-17
  11. Noh, M., Ha, I. D., & Lee, Y. (2006). Dispersion Frailty Models and HGLMs. Statistic in Medicine, 25(8): 1341-1354
  12. Porndumnernsawat, P., Frank, T.D. & Ingsrisawang, L. (2025). On A Bayesian Multivariate Survival Tree Approach Based on Three Frailty Models. Sci Rep 15, 12017
  13. Vaupel, J. W., Manton, K. G., & Stallard, E. (1979). The Impact of Heterogenity in Individual Frailty on The Dynamics of Mortality. Demography, 16(3): 439-454
  14. Yslas, J. (2025). Phase-Type Frailty Models: A Flexible Approach to Modeling Unobserved Heterogeneity in Survival Analysis. Scandinavian Actuarial Journal, 1-29
  15. Zhang, Y., Li, X., & Wang, J. (2020). A Bayesian Semiparametric Model for Multivariate Frailty. Statistica Sinica, 30(1), 185–206
  16. Zhou, H., Hanson, T., Jara, A., & Zhang, J. (2015). Modeling County Level Breast Cancer Survival Data Using a Covariate-Adjusted Frailty Proportional Hazards Model. The Annals of Applied Statistics, 9(1): 43-68

Last update:

No citation recorded.

Last update: 2025-10-16 20:54:02

No citation recorded.