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IJE Advance Access published online on December 20, 2007
International Journal of Epidemiology, doi:10.1093/ije/dym232
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Published by Oxford University Press on behalf of the International Epidemiological Association © The Author 2007; all rights reserved.

Measuring effectiveness in community randomized trials of HIV prevention

TB Hallett1,*, GP Garnett1, Z Mupamberiyi2 and S Gregson1,2

1Department of Infectious Disease Epidemiology, Imperial College London, UK.
2Biomedical Research and Training Institute, Harare, Zimbabwe.

* Corresponding author. Department of Infectious Disease Epidemiology, Imperial College London, St Mary's Campus, Norfolk Place, London, W2 1PG, UK. E-mail: timothy.hallett{at}imperial.ac.uk


  Abstract

Background Complicated HIV transmission dynamics make it unclear how to design and interpret results from community-randomized controlled trials (CRCT) of interventions to prevent infection.

Methods Mathematical modelling was used to investigate the effectiveness of interventions to prevent HIV transmission aimed at high-risk groups and factors related to the chance of recording a statistically significant result.

Results Behaviour change by high-risk groups can substantially reduce HIV incidence in the whole population, although its effect is sensitive to the structure of the sexual network and the phase of the epidemic. There is a delay between the behaviour change happening and its full effect being realized in the low-risk group and this can pull the measured incidence rate ratio towards one and reduce the chance of recording a statistically significant result in a CRCT. Our simulations suggest that only with unrealistically favourable study conditions would a statistically significant result be likely with 5 years follow-up or less. Small differences in the epidemiological parameters between communities can lead to misleading incidence rate ratios. Behaviour change independent of the intervention can increase the epidemiological impact of the intervention and the chance of recording a statistically significant result.

Conclusions HIV prevention interventions, especially those targeted at high-risk groups may take longer to work at the population level and need more follow-up time in a CRCT to generate statistically significant results. Mathematical modelling can be used in the design and analysis of CRCTs to understand how the impact of the intervention could develop and the implications this has for statistical power.

Keywords HIV, randomized controlled trials, statistical power

Accepted 16 October 2007


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