Literature DB >> 17573863

Proportional hazards regression for cancer studies.

Debashis Ghosh1.   

Abstract

There has been some recent work in the statistical literature for modeling the relationship between the size of cancers and probability of detecting metastasis, i.e., aggressive disease. Methods for assessing covariate effects in these studies are limited. In this article, we formulate the problem as assessing covariate effects on a right-censored variable subject to two types of sampling bias. The first is the length-biased sampling that is inherent in screening studies; the second is the two-phase design in which a fraction of tumors are measured. We construct estimation procedures for the proportional hazards model that account for these two sampling issues. In addition, a Nelson-Aalen type estimator is proposed as a summary statistic. Asymptotic results for the regression methodology are provided. The methods are illustrated by application to data from an observational cancer study as well as to simulated data.

Entities:  

Mesh:

Year:  2007        PMID: 17573863     DOI: 10.1111/j.1541-0420.2007.00830.x

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


  9 in total

1.  A maximum pseudo-profile likelihood estimator for the Cox model under length-biased sampling.

Authors:  Chiung-Yu Huang; Jing Qin; Dean A Follmann
Journal:  Biometrika       Date:  2012-01-27       Impact factor: 2.445

2.  Proportional hazards model with varying coefficients for length-biased data.

Authors:  Feipeng Zhang; Xuerong Chen; Yong Zhou
Journal:  Lifetime Data Anal       Date:  2013-05-07       Impact factor: 1.588

Review 3.  Recent progresses in outcome-dependent sampling with failure time data.

Authors:  Jieli Ding; Tsui-Shan Lu; Jianwen Cai; Haibo Zhou
Journal:  Lifetime Data Anal       Date:  2016-01-13       Impact factor: 1.588

Review 4.  Nonparametric and semiparametric regression estimation for length-biased survival data.

Authors:  Yu Shen; Jing Ning; Jing Qin
Journal:  Lifetime Data Anal       Date:  2016-04-16       Impact factor: 1.588

5.  A Bayesian semiparametric method for analyzing length-biased data.

Authors:  Nusrat Harun; Bo Cai; Yu Shen
Journal:  J Appl Stat       Date:  2020-04-14       Impact factor: 1.416

6.  Semiparametric regression in size-biased sampling.

Authors:  Ying Qing Chen
Journal:  Biometrics       Date:  2009-05-04       Impact factor: 2.571

7.  Inference for constrained estimation of tumor size distributions.

Authors:  Debashis Ghosh; Moulinath Banerjee; Pinaki Biswas
Journal:  Biometrics       Date:  2008-03-27       Impact factor: 2.571

8.  Statistical methods for analyzing right-censored length-biased data under cox model.

Authors:  Jing Qin; Yu Shen
Journal:  Biometrics       Date:  2009-06-12       Impact factor: 2.571

9.  Assessing risk factors of acute kidney injury and its influence on adverse outcomes after lung transplantation: methodology is important.

Authors:  Bin Hu; Fu-Shan Xue; Shao-Hua Liu; Yi Cheng
Journal:  Ren Fail       Date:  2021-12       Impact factor: 2.606

  9 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.