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Cluster-randomized trials (CRTs) of infectious disease progression often result in data where individuals belonging to the same contact networks and communities are more likely to be similar to one another. In addition, their infection status may be assessed only at intermittent study visits. The design, monitoring and analysis of these CRTs must account for this data structure. I will discuss a flexible, simulation-based framework for conducting interim monitoring when outcomes are correlated and interval-censored and will show that this approach produces valid estimates of a trial’s ultimate probability of success (termed the conditional power) across a range of data-generating mechanisms and CRT design considerations. The framework also has high accuracy in classifying trials as futile based on available interim data. I will illustrate its use by applying it to the Botswana Combination Prevention Project, a cluster-randomized HIV prevention trial.

Refreshments at 4:15 p.m.
Talk at 4:30 p.m.

Contact Info

Nicholas Horton
(413) 542-5655
Please call the college operator at 413-542-2000 or e-mail if you require contact info