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American Journal of Epidemiology Advance Access published online on February 16, 2006
American Journal of Epidemiology, doi:10.1093/aje/kwj083
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American Journal of Epidemiology Copyright © 2006 by the Johns Hopkins Bloomberg School of Public Health All rights reserved; printed in U.S.A.
Received July 15, 2005
Accepted October 27, 2005

PRACTICE OF EPIDEMIOLOGY

The Effect on Treatment Comparisons of Different Measurement Frequencies in Human Immunodeficiency Virus Observational Databases

James T. Griffin 1, Christophe Fraser 1, Luuk Gras 2, Frank de Wolf 3, and Azra C. Ghani 4 *

1 Department of Infectious Disease Epidemiology, Faculty of Medicine, Imperial College, London, United Kingdom
2 HIV Monitoring Foundation, Amsterdam, the Netherlands
3 Department of Infectious Disease Epidemiology, Faculty of Medicine, Imperial College, London, United Kingdom; HIV Monitoring Foundation, Amsterdam, the Netherlands
4 Infectious Disease Epidemiology Unit, Department of Infectious and Tropical Diseases, London School of Hygiene and Tropical Medicine, London, United Kingdom

* To whom correspondence should be addressed.
Azra C. Ghani, E-mail: azra.ghani{at}lshtm.ac.uk


  Abstract

Data collected in a routine clinical setting are frequently used to compare antiretroviral treatments for human immunodeficiency virus (HIV). Differences in the frequency of measurement of HIV RNA levels and CD4-positive T-lymphocyte cell counts introduce a possible source of bias into estimates of the difference in effectiveness between treatments. The authors investigated the size of this bias when survival analysis methods are used to compare the initial efficacy of antiretroviral regimens. Data sets of clinical markers were simulated by use of differential equations that model the interaction between HIV and human T-cells. Cox proportional hazards and parametric models were fitted to the simulated data sets to evaluate the bias and coverage of 95% confidence intervals for the difference between regimens. The authors' results demonstrate that differences in the frequency of follow-up can substantially bias estimated treatment differences if methods do not correctly account for the intervals between measurements and if the statistical model chosen does not fit the data well. Analyses using methods applicable to interval-censored data reduce the bias. In the Athena cohort of HIV-infected individuals in the Netherlands from 1999 to 2003, there are differences in measurement frequency between current regimens that are of sufficient magnitude to conclude incorrectly that some regimens are more effective than others.

Keywords: antiretroviral therapy, highly active; bias (epidemiology); cohort studies; HIV; survival analysis.
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