Influence diagnostics for random effect survival models: Application to a recurrent infection study for kidney patients on portable dialysis |
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| Affiliation: | 1. Centre for Artificial Intelligence, Faculty of Engineering and Information Technology, University of Technology Sydney, PO Box 123, Broadway, NSW 2007, Sydney, Australia;2. School of Computer Engineering and Science, Shanghai University, 99 Shangda Road, Shanghai, China;1. Institute for Systems and Robotics, University of Lisbon, Portugal;2. Institute for Systems and Robotics, University of Coimbra, Portugal;1. School of Natural and Mathematical Sciences, King''s College London, Strand, London WC2R 2LS, UK;2. DH-Robotics Technology Co. Ltd, Shenzhen 518063, Guangdong, China |
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| Abstract: | In modeling multivariate failure time data, a class of survival model with random effects is applicable. It incorporates the random effect terms in the linear predictor and includes various random effect survival models as special cases, such as the random effect model assuming Cox's proportional hazards, with Weibull baseline hazards and with power family of transformation in the relative risk function. Residual maximum likelihood (REML) estimation of parameters is achieved by adopting the generalised linear mixed models (GLMM) approach. Accordingly, influence diagnostics are developed as sensitivity measures for the REML estimation of model parameters. A data set of recurrent infections of kidney patients on portable dialysis illustrates the usefulness of the influence diagnostics. A simulation study is carried out to examine the performance of the proposed influence diagnostics. |
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