Literature DB >> 16708345

Event history analysis and the cross-section.

Niels Keiding1.   

Abstract

Examples are given of problems in event history analysis, where several time origins (generating calendar time, age, disease duration, time on study, etc.) are considered simultaneously. The focus is on complex sampling patterns generated around a cross-section. A basic tool is the Lexis diagram.

Mesh：

Year:  2006        PMID： 16708345     DOI： 10.1002/sim.2579

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


  16 in total

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

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

2.  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

3.  Prevalent cohort studies and unobserved heterogeneity.

Authors:  Niels Keiding; Katrine Lykke Albertsen; Helene Charlotte Rytgaard; Anne Lyngholm Sørensen
Journal:  Lifetime Data Anal       Date:  2019-07-03       Impact factor: 1.588

4.  Parametric modelling of prevalent cohort data with uncertainty in the measurement of the initial onset date.

Authors:  J H McVittie; D B Wolfson; D A Stephens
Journal:  Lifetime Data Anal       Date:  2019-08-02       Impact factor: 1.588

5.  Illness-death model: statistical perspective and differential equations.

Authors:  Ralph Brinks; Annika Hoyer
Journal:  Lifetime Data Anal       Date:  2018-01-27       Impact factor: 1.588

6.  Estimating age-specific incidence of dementia using prevalent cohort data.

Authors:  Binbing Yu
Journal:  J Stat Comput Simul       Date:  2011-08       Impact factor: 1.424

7.  Estimation of covariate effects with current status data and differential mortality.

Authors:  Alberto Palloni; Jason R Thomas
Journal:  Demography       Date:  2013-04

8.  A new relation between prevalence and incidence of a chronic disease.

Authors:  Ralph Brinks; Sandra Landwehr
Journal:  Math Med Biol       Date:  2015-01-09       Impact factor: 1.854

9.  A risk set adjustment for proportional hazards modeling of combined cohort data.

Authors:  J H McVittie; V Addona
Journal:  J Appl Stat       Date:  2021-05-12       Impact factor: 1.416

10.  Hypothesis Tests for Neyman's Bias in Case-Control Studies.

Authors:  D M Swanson; C D Anderson; R A Betensky
Journal:  J Appl Stat       Date:  2017-11-16       Impact factor: 1.404
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