Literature DB >> 15565735

Statistical analysis of current status data with informative observation times.

Zhigang Zhang1, Jianguo Sun, Liuquan Sun.   

Abstract

Current status data arise when each study subject is observed only once and the survival time of interest is known only to be either less or greater than the observation time. Such data often occur in, for example, cross-sectional studies, demographical investigations and tumorigenicity experiments and several semi-parametric and non-parametric methods for their analysis have been proposed. However, most of these methods deal only with the situation where observation time is independent of the underlying survival time completely or given covariates. This paper discusses regression analysis of current status data when the observation time may be related to the underlying survival time and inference procedures are presented for estimation of regression parameters under the additive hazards regression model. The procedures can be easily implemented and are applied to two motivating examples. Copyright 2004 John Wiley & Sons, Ltd.

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Year:  2005        PMID: 15565735     DOI: 10.1002/sim.2001

Source DB:  PubMed          Journal:  Stat Med        ISSN: 0277-6715            Impact factor:   2.373


  12 in total

1.  Regression analysis of longitudinal data with correlated censoring and observation times.

Authors:  Yang Li; Xin He; Haiying Wang; Jianguo Sun
Journal:  Lifetime Data Anal       Date:  2015-06-30       Impact factor: 1.588

2.  Regression analysis of current status data with auxiliary covariates and informative observation times.

Authors:  Yanqin Feng; Yurong Chen
Journal:  Lifetime Data Anal       Date:  2017-01-05       Impact factor: 1.588

3.  Regression analysis of current status data in the presence of a cured subgroup and dependent censoring.

Authors:  Yeqian Liu; Tao Hu; Jianguo Sun
Journal:  Lifetime Data Anal       Date:  2016-09-30       Impact factor: 1.588

4.  REGRESSION ANALYSIS OF CASE II INTERVAL-CENSORED FAILURE TIME DATA WITH THE ADDITIVE HAZARDS MODEL.

Authors:  Lianming Wang; Jianguo Sun; Xingwei Tong
Journal:  Stat Sin       Date:  2010       Impact factor: 1.261

5.  Nonparametric estimation of current status data with dependent censoring.

Authors:  Chunjie Wang; Jianguo Sun; Liuquan Sun; Jie Zhou; Dehui Wang
Journal:  Lifetime Data Anal       Date:  2012-06-27       Impact factor: 1.588

6.  Regression analysis of informative current status data with the additive hazards model.

Authors:  Shishun Zhao; Tao Hu; Ling Ma; Peijie Wang; Jianguo Sun
Journal:  Lifetime Data Anal       Date:  2014-07-31       Impact factor: 1.588

7.  Semiparametric regression analysis of interval-censored data with informative dropout.

Authors:  Fei Gao; Donglin Zeng; Dan-Yu Lin
Journal:  Biometrics       Date:  2018-06-05       Impact factor: 2.571

8.  Joint analysis of interval-censored failure time data and panel count data.

Authors:  Da Xu; Hui Zhao; Jianguo Sun
Journal:  Lifetime Data Anal       Date:  2017-06-12       Impact factor: 1.588

9.  Semiparametric Random Effects Models for Longitudinal Data with Informative Observation Times.

Authors:  Yang Li; Yanqing Sun
Journal:  Stat Interface       Date:  2016       Impact factor: 0.582

Review 10.  Interval censoring.

Authors:  Zhigang Zhang; Jianguo Sun
Journal:  Stat Methods Med Res       Date:  2009-08-04       Impact factor: 3.021

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