Literature DB >> 6648142

Analysis of survival data by the proportional odds model.

S Bennett.   

Abstract

A model is presented for the analysis of lifetime data in which the rates of mortality for separate groups of patients converge with time. A non-parametric estimate is given for the survivor function. The theoretical basis for the model assumes that prognostic factors have a multiplicative effect on the odds against survival beyond any given time. The model is fitted to data using maximum likelihood estimation, and an example of its use in the analysis of a lung cancer trial is given.

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Year:  1983        PMID: 6648142     DOI: 10.1002/sim.4780020223

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


  63 in total

1.  Marginal likelihood estimation for proportional odds models with right censored data.

Authors:  K F Lam; T L Leung
Journal:  Lifetime Data Anal       Date:  2001-03       Impact factor: 1.588

2.  A profile conditional likelihood approach for the semiparametric transformation regression model with missing covariates.

Authors:  H Y Chen; R J Little
Journal:  Lifetime Data Anal       Date:  2001-09       Impact factor: 1.588

3.  Estimation of treatment effects based on possibly misspecified Cox regression.

Authors:  Satoshi Hattori; Masayuki Henmi
Journal:  Lifetime Data Anal       Date:  2012-04-21       Impact factor: 1.588

4.  A general framework for studying genetic effects and gene-environment interactions with missing data.

Authors:  Y J Hu; D Y Lin; D Zeng
Journal:  Biostatistics       Date:  2010-03-26       Impact factor: 5.899

5.  A varying-coefficient generalized odds rate model with time-varying exposure: An application to fitness and cardiovascular disease mortality.

Authors:  Jie Zhou; Jiajia Zhang; Alexander C Mclain; Wenbin Lu; Xuemei Sui; James W Hardin
Journal:  Biometrics       Date:  2019-06-17       Impact factor: 2.571

6.  Marginal regression of multivariate event times based on linear transformation models.

Authors:  Wenbin Lu
Journal:  Lifetime Data Anal       Date:  2005-09       Impact factor: 1.588

7.  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

8.  Efficient semiparametric estimation of short-term and long-term hazard ratios with right-censored data.

Authors:  Guoqing Diao; Donglin Zeng; Song Yang
Journal:  Biometrics       Date:  2013-11-04       Impact factor: 2.571

9.  ROBUST MIXED EFFECTS MODEL FOR CLUSTERED FAILURE TIME DATA: APPLICATION TO HUNTINGTON'S DISEASE EVENT MEASURES.

Authors:  Tanya P Garcia; Yanyuan Ma; Karen Marder; Yuanjia Wang
Journal:  Ann Appl Stat       Date:  2017-07-20       Impact factor: 2.083

10.  Semiparametric odds rate model for modeling short-term and long-term effects with application to a breast cancer genetic study.

Authors:  Mengdie Yuan; Guoqing Diao
Journal:  Int J Biostat       Date:  2014       Impact factor: 0.968

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