Survival analysis with longitudinal covariates measured with correlated errors.
Speaker: Prof. Jianxin Pan, University of Manchester
Time: 4.00PM
Date: Thursday 30th October 2014
Location: Seminar Room L504, 5th floor, Library Building
Abstract: When covariates in Cox's proportional hazards model are time-dependent, statistical inferences may be similar to those with time-independent covariates provided that complete knowledge of the true covariates history is available. Time-dependent covariates, however, are usually measured intermittently and very likely to be measured with errors, so that joint modelling of survival and longitudinal data is much preferred. The existing methods such as sufficient statistical methods assume that longitudinal covariates are measured with mutually independent errors, which unfortunately is not always true in practices. In this research, it is evident through simulation studies that violation of the independent errors assumption can lead to very biased estimates of regression coefficients. Generalized least square estimates, rather than ordinary least square estimates, are adopted for time-dependent covariates to account for correlated measurement errors. Furthermore, covariance modelling strategy based on modified Cholesky decomposition is proposed to model the covariance structure of the measurement errors. Simulation studies show that the proposed method performs very well. Real data analysis is provided too.
Series: Statistics Seminar Series
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