Literature DB >> 3830259

A semiparametric model for regression analysis of interval-censored failure time data.

D M Finkelstein, R A Wolfe.   

Abstract

Left-, right-, and interval-censored response time data arise in a variety of settings, including the analyses of data from laboratory animal carcinogenicity experiments, clinical trials, and longitudinal studies. For such incomplete data, the usual regression techniques such as the Cox (1972, Journal of the Royal Statistical Society, Series B 34, 187-220) proportional hazards model are inapplicable. In this paper, we present a method for regression analysis which accommodates interval-censored data. We present applications of this methodology to data sets from a study of breast cancer patients who were followed for cosmetic response to therapy, a small animal tumorigenicity study, and a clinical trial.

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Year:  1985        PMID: 3830259

Source DB:  PubMed          Journal:  Biometrics        ISSN: 0006-341X            Impact factor:   2.571


  17 in total

Review 1.  Rank estimation of log-linear regression with interval-censored data.

Authors:  Linxiong Li; Zongwei Pu
Journal:  Lifetime Data Anal       Date:  2003-03       Impact factor: 1.588

2.  A baseline-free procedure for transformation models under interval censorship.

Authors:  Ming Gao Gu; Liuquan Sun; Guoxin Zuo
Journal:  Lifetime Data Anal       Date:  2005-12       Impact factor: 1.588

3.  "Smooth" semiparametric regression analysis for arbitrarily censored time-to-event data.

Authors:  Min Zhang; Marie Davidian
Journal:  Biometrics       Date:  2007-10-25       Impact factor: 2.571

4.  A study of interval censoring in parametric regression models.

Authors:  J K Lindsey
Journal:  Lifetime Data Anal       Date:  1998       Impact factor: 1.588

5.  Renovating interval-censored responses.

Authors:  P J Smith
Journal:  Lifetime Data Anal       Date:  1996       Impact factor: 1.588

6.  Nonparametric estimation of the cumulative intensities in an interval censored competing risks model.

Authors:  Halina Frydman; Jun Liu
Journal:  Lifetime Data Anal       Date:  2012-10-09       Impact factor: 1.588

7.  An Expectation Maximization algorithm for fitting the generalized odds-rate model to interval censored data.

Authors:  Jie Zhou; Jiajia Zhang; Wenbin Lu
Journal:  Stat Med       Date:  2016-12-21       Impact factor: 2.373

8.  An empirical comparison of key statistical attributes among potential ICU quality indicators.

Authors:  Sydney E S Brown; Sarah J Ratcliffe; Scott D Halpern
Journal:  Crit Care Med       Date:  2014-08       Impact factor: 7.598

9.  'Smooth' inference for survival functions with arbitrarily censored data.

Authors:  Kirsten Doehler; Marie Davidian
Journal:  Stat Med       Date:  2008-11-20       Impact factor: 2.373

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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