Literature DB >> 8513095

Statistical models for prevalent cohort data.

M C Wang1, R Brookmeyer, N P Jewell.   

Abstract

In prospective cohort studies individuals are sometimes recruited according to a certain cross-sectional sampling criterion. A prevalent cohort is defined as a group of individuals who have a certain disease at enrollment into the study. Statistical models for the analysis of prevalent cohort data are considered when the onset or diagnosis time of the disease is known. The incident proportional hazards model, where the time scale is duration with disease, is compared to the prevalent proportional hazards model, where the fundamental time scale is follow-up time. In certain cases the time of enrollment may coincide with another event (such as the initiation of treatment). This situation is also considered and its limitations highlighted. To illustrate the methodological ideas discussed in the paper, the analysis of data from an observational study of zidovudine (ZVD) in patients with the acquired immunodeficiency syndrome (AIDS) is presented.

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Year:  1993        PMID: 8513095

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


  47 in total

1.  Marginal structural models for case-cohort study designs to estimate the association of antiretroviral therapy initiation with incident AIDS or death.

Authors:  Stephen R Cole; Michael G Hudgens; Phyllis C Tien; Kathryn Anastos; Lawrence Kingsley; Joan S Chmiel; Lisa P Jacobson
Journal:  Am J Epidemiol       Date:  2012-02-01       Impact factor: 4.897

2.  Natural history of diseases: Statistical designs and issues.

Authors:  Nicholas P Jewell
Journal:  Clin Pharmacol Ther       Date:  2016-08-18       Impact factor: 6.875

3.  Relationship of medial temporal lobe atrophy, APOE genotype, and cognitive reserve in preclinical Alzheimer's disease.

Authors:  Anja Soldan; Corinne Pettigrew; Yi Lu; Mei-Cheng Wang; Ola Selnes; Marilyn Albert; Timothy Brown; J Tilak Ratnanather; Laurent Younes; Michael I Miller
Journal:  Hum Brain Mapp       Date:  2015-04-16       Impact factor: 5.038

4.  Estimating incident population distribution from prevalent data.

Authors:  Kwun Chuen Gary Chan; Mei-Cheng Wang
Journal:  Biometrics       Date:  2012-02-07       Impact factor: 2.571

5.  Mismatch repair gene polymorphisms and survival in invasive ovarian cancer patients.

Authors:  Andrea Mann; Estrid Hogdall; Susan J Ramus; Richard A DiCioccio; Claus Hogdall; Lydia Quaye; Valerie McGuire; Alice S Whittemore; Mitul Shah; David Greenberg; Douglas F Easton; Bruce A J Ponder; Susanne Krüger Kjaer; Simon A Gayther; Deborah J Thompson; Paul D P Pharoah; Honglin Song
Journal:  Eur J Cancer       Date:  2008-08-22       Impact factor: 9.162

6.  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

7.  Proportional mean residual life model for right-censored length-biased data.

Authors:  Kwun Chuen Gary Chan; Ying Qing Chen; Chong-Zhi Di
Journal:  Biometrika       Date:  2012-09-30       Impact factor: 2.445

8.  Nonparametric incidence estimation from prevalent cohort survival data.

Authors:  Marco Carone; Masoud Asgharian; Mei-Cheng Wang
Journal:  Biometrika       Date:  2012-07-24       Impact factor: 2.445

9.  Imputation for semiparametric transformation models with biased-sampling data.

Authors:  Hao Liu; Jing Qin; Yu Shen
Journal:  Lifetime Data Anal       Date:  2012-08-18       Impact factor: 1.588

10.  Sample size calculations for prevalent cohort designs.

Authors:  Hao Liu; Yu Shen; Jing Ning; Jing Qin
Journal:  Stat Methods Med Res       Date:  2016-07-11       Impact factor: 3.021

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