Skip to content

Journal Article

Linear transformation models for censored data under truncation

Authors

Publication date

Feb 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

193 , 42 -54

DOI

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

ISSN

16

Notes

Not held in Hilary Doughty Research Library - bibliographic reference only

#524570


Research home

Research home

News

Latest findings, new research

Publications search

Search all research by subject and author

Podcasts

Researchers discuss their findings and what they mean for society

Projects

Background and context, methods and data, aims and outputs

Events

Conferences, seminars and workshops

Survey methodology

Specialist research, practice and study

Taking the long view

ISER's annual report

Themes

Key research themes and areas of interest