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Modeling the rate of HIV testing from repeated binary data amidst potential never-testers

John D Rice1, Brent A Johnson2, Robert L Strawderman2

  • 1University of Rochester Medical Center, Biostatistics and Computational Biology, 265 Crittenden Blvd., Box 630, Rochester, NY 14642, USA.

Biostatistics (Oxford, England)
|January 10, 2018
PubMed
Summary

This study introduces a new statistical model for analyzing event rates in longitudinal data, particularly for identifying "never-responders." The model found that SMS follow-up messages reduced human immunodeficiency virus (HIV) self-testing rates among men who have sex with men (MSM).

Keywords:
Binary longitudinal dataFrailty modelsHIV testingPower variance functionRecurrent events

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