Influence diagnostics for random effect survival models: application to a recurrent infection study for kidney patients on portable dialysis

DOI10.1016/J.CSDA.2006.11.023zbMath1445.62299OpenAlexW1992453392MaRDI QIDQ1020701

Liming Xiang, Andy H. Lee, Kelvin. K. W. Yau, Siu-Keung Tse

Publication date: 2 June 2009

Published in: Computational Statistics and Data Analysis (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.csda.2006.11.023

zbMATH Keywords

random effectsinfluence diagnosticslocal influenceWeibull distributionmultivariate failure timesgeneralised linear mixed models

Mathematics Subject Classification ID

Applications of statistics to biology and medical sciences; meta analysis (62P10) Generalized linear models (logistic models) (62J12) Estimation in survival analysis and censored data (62N02) Diagnostics, and linear inference and regression (62J20)

Related Items (3)

Bayesian threshold regression model with random effects for recurrent events ⋮ Assessing the adequacy of Weibull survival models: a simulated envelope approach ⋮ Extending the long-term survivor mixture model with random effects for clustered survival data

Cites Work

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• Power family of transformation for Cox's regression with random effects.
• Influence diagnostics for generalized linear mixed models: applications to clustered data.
• Multilevel Models for Survival Analysis with Random Effects
• Functional Form Diagnostics for Cox's Proportional Hazards Model
• Use of Generalised Linear Mixed Models for the Analysis of Clustered Survival Data
• Global Measures of Local Influence for Proportional Hazards Regression Models
• Masking Unmasked in the Proportional Hazards Model
• A Diagnostic for Cox Regression with Discrete Failure‐Time Models

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