Please use this identifier to cite or link to this item: http://hdl.handle.net/1942/13785
Title: Continuous covariate frailty models for censored and truncated clustered data
Authors: Lopez-de-Ullibarri, Ignacio
JANSSEN, Paul 
Cao, Ricardo
Issue Date: 2012
Publisher: ELSEVIER SCIENCE BV
Source: JOURNAL OF STATISTICAL PLANNING AND INFERENCE, 142 (7), p. 1864-1877
Abstract: Using some logarithmic and integral transformation we transform a continuous covariate frailty model into a polynomial regression model with a random effect. The responses of this mixed model can be 'estimated' via conditional hazard function estimation. The random error in this model does not have zero mean and its variance is not constant along the covariate and, consequently, these two quantities have to be estimated. Since the asymptotic expression for the bias is complicated, the two-large-bandwidth trick is proposed to estimate the bias. The proposed transformation is very useful for clustered incomplete data subject to left truncation and right censoring (and for complex clustered data in general). Indeed, in this case no standard software is available to fit the frailty model, whereas for the transformed model standard software for mixed models can be used for estimating the unknown parameters in the original frailty model. A small simulation study illustrates the good behavior of the proposed method. This method is applied to a bladder cancer data set.
Notes: [Lopez-de-Ullibarri, Ignacio] Univ Coruna, Escuela Univ Politecn, Dept Math, Ferrol, A Coruna, Spain. [Janssen, Paul] Hasselt Univ, Ctr Stat, B-3590 Diepenbeek, Belgium. [Cao, Ricardo] Univ Coruna, Fac Informat, Dept Math, Ferrol, A Coruna, Spain.
Keywords: Conditional hazard function; Clustered survival data; Estimation via transformation; Kernel estimation;Statistics & Probability; Conditional hazard function; Clustered survival data; Estimation via transformation; Kernel estimation
Document URI: http://hdl.handle.net/1942/13785
ISSN: 0378-3758
e-ISSN: 1873-1171
DOI: 10.1016/j.jspi.2012.02.044
ISI #: 000304074500022
Category: A1
Type: Journal Contribution
Validations: ecoom 2013
Appears in Collections:Research publications

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