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A Comparison of Cox and Joint Models for Time-to-Event Data

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Title: A Comparison of Cox and Joint Models for Time-to-Event Data
Author: Stefan, George
Department: Department of Mathematics and Statistics
Program: Mathematics and Statistics
Advisor: Horrocks, Julie
Abstract: The Cox model has traditionally been used to analyse the relationship between a set of covariates and a time-to-event outcome. However, it has been found to lead to biased estimates when fitting time-varying covariates subject to measurement error. Joint modelling procedures have thus been developed with the purpose of alleviating this problem. Simulations were performed in which we compared bias and variability in parameter estimates between the Cox model and Rizopoulos (2010) joint model, concluding that the Cox model is heavily biased when the data is generated according to a joint model. Furthermore, we analysed medical data which recorded the onset time of bipolar/major mood disorder (in years since birth) for subjects who were considered at risk. The Cox model and both joint models agreed that a time-varying covariate, the Hamilton anxiety score, had a significant effect (at a 5% level) on the risk of bipolar/major mood disorder.
Date: 2019
Rights: Attribution-ShareAlike 4.0 International

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Attribution-ShareAlike 4.0 International Except where otherwise noted, this item's license is described as Attribution-ShareAlike 4.0 International