A class of transformed hazards models for recurrent gap times

Fangyuan Kang; Liuquan Sun; Xingqiu Zhao

doi:10.1016/j.csda.2014.10.005

A class of transformed hazards models for recurrent gap times

Fangyuan Kang, Liuquan Sun, Xingqiu Zhao

Research output: Journal article publication › Journal article › Academic research › peer-review

9 Citations (Scopus)

Abstract

In this article, a class of transformed hazards models is proposed for recurrent gap time data, including both the proportional and additive hazards models as special cases. An estimating equation-based inference procedure is developed for the model parameters, and the asymptotic properties of the resulting estimators are established. In addition, a lack-of-fit test is presented to assess the adequacy of the model. The finite sample behavior of the proposed estimators is evaluated through simulation studies, and an application to a clinic study on chronic granulomatous disease (CGD) is illustrated.

Original language	English
Pages (from-to)	151-167
Number of pages	17
Journal	Computational Statistics and Data Analysis
Volume	83
DOIs	https://doi.org/10.1016/j.csda.2014.10.005
Publication status	Published - 1 Jan 2015

Keywords

Estimating equations
Gap times
Model checking
Recurrent events
Transformed hazards model

ASJC Scopus subject areas

Statistics and Probability
Computational Mathematics
Computational Theory and Mathematics
Applied Mathematics

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10.1016/j.csda.2014.10.005

Cite this

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AB - In this article, a class of transformed hazards models is proposed for recurrent gap time data, including both the proportional and additive hazards models as special cases. An estimating equation-based inference procedure is developed for the model parameters, and the asymptotic properties of the resulting estimators are established. In addition, a lack-of-fit test is presented to assess the adequacy of the model. The finite sample behavior of the proposed estimators is evaluated through simulation studies, and an application to a clinic study on chronic granulomatous disease (CGD) is illustrated.

KW - Estimating equations

KW - Gap times

KW - Model checking

KW - Recurrent events

KW - Transformed hazards model

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JO - Computational Statistics and Data Analysis

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ER -

A class of transformed hazards models for recurrent gap times

Abstract

Keywords

ASJC Scopus subject areas

More information

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