Skip Navigation

This Article
Right arrow Full Text Freely available
Right arrow FREE Full Text (PDF) Freely available
Right arrow Alert me when this article is cited
Right arrow Alert me if a correction is posted
Services
Right arrow Email this article to a friend
Right arrow Similar articles in this journal
Right arrow Similar articles in ISI Web of Science
Right arrow Similar articles in PubMed
Right arrow Alert me to new issues of the journal
Right arrow Add to My Personal Archive
Right arrow Download to citation manager
Right arrow Search for citing articles in:
ISI Web of Science (5)
Right arrowRequest Permissions
Right arrow Disclaimer
Google Scholar
Right arrow Articles by Williamson, J. M.
Right arrow Articles by Datta, S.
Right arrow Search for Related Content
PubMed
Right arrow PubMed Citation
Right arrow Articles by Williamson, J. M.
Right arrow Articles by Datta, S.
Social Bookmarking
Add to CiteULike   Add to Connotea   Add to Del.icio.us  
What's this?

American Journal of Epidemiology Vol. 154, No. 4 : 366-372
Copyright © 2001 by The Johns Hopkins University School of Hygiene and Public Health

ORIGINAL CONTRIBUTIONS

Analysis of Dynamic Cohort Data

John M. Williamson1, Glen A. Satten1,4, Jeffrey A. Hanson2, Hillard Weinstock1 and Somnath Datta3

1 Division of HIV/AIDS Prevention: Surveillance and Epidemiology, National Center for HIV, STD and TB Prevention, Centers for Disease Control and Prevention, Atlanta, GA.
2 HIV/AIDS Program, Louisiana Office of Public Health, New Orleans, LA.
3 Department of Statistics, University of Georgia, Athens, GA.
4 Present affiliation: National Center for Environmental Health, Centers for Disease Control and Prevention, Atlanta, GA.

Left-truncated and interval-censored data, termed dynamic cohort data, arise in longitudinal studies with rolling admissions and only occasional follow-up. The authors compared four approaches for analyzing such data: a constant hazard model; maximum likelihood estimation with flexible parametric models; the midpoint method, in which the midpoint of the last negative and first positive test result is used in a Cox proportional hazards model that accounts for left truncation; and a semiparametric method that uses imputed failure times in the Cox model. By using a simulation study, they assessed the performance of these approaches under conditions that can arise in observational studies: changes in disease incidence and changes in the underlying population. The simulation results indicated that the constant hazard model and midpoint method were inadequate and that the flexible parametric model was useful when enough parameters were used in modeling the baseline hazard. The semiparametric method ensured correct parameter (odds ratio) estimation when the baseline hazard was misspecified, but the trade-off increased computational complexity. In this paper, a study of the incidence of human immunodeficiency virus in patients repeatedly tested for the virus at a sexually transmitted disease clinic in New Orleans, Louisiana, illustrates the methods used.

Cox regression; incidence; interval censoring; survival analysis; truncation

Abbreviations: AIDS, acquired immunodeficiency syndrome; HIV, human immunodeficiency virus; STD, sexually transmitted disease


Add to CiteULike CiteULike   Add to Connotea Connotea   Add to Del.icio.us Del.icio.us    What's this?


This article has been cited by other articles:


Home page
 Jpn J Clin OncolHome page
J. Sakamoto and S. Teramukai
Data Handling in Cancer Clinical Trials--How We Can Minimize Potential Biases
Jpn. J. Clin. Oncol., January 1, 2002; 32(1): 1 - 2.
[Full Text] [PDF]


Disclaimer:
Please note that abstracts for content published before 1996 were created through digital scanning and may therefore not exactly replicate the text of the original print issues. All efforts have been made to ensure accuracy, but the Publisher will not be held responsible for any remaining inaccuracies. If you require any further clarification, please contact our Customer Services Department.