Linear transformation models for censored data under truncation

Publication type

Journal Article

Authors

Publication date

February 15, 2018

Summary:

In many observational cohort studies, a pair of correlated event times are usually observed for each individual. This paper develops a new approach for the semiparametric linear transformation model to handle the bivariate survival data under both truncation and censoring. By incorporating truncation, the potential referral bias in practice is taken into account. A class of generalised estimating equations are proposed to obtain unbiased estimates of the regression parameters. Large sample properties of the proposed estimator are provided. Simulation studies under different scenarios and analyses of real-world datasets are conducted to assess the performance of the proposed estimator.

Published in

Journal of Statistical Planning and Inference

Volume and page numbers

Volume: 193 , p.42 -54

DOI

http://dx.doi.org/10.1016/j.jspi.2017.07.006

ISSN

3783758

Subject

Notes

Not held in Hilary Doughty Research Library - bibliographic reference only

#524570

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