Abstract
There is a need to better understand the effectiveness of HIV-prevention programs. Cluster randomized designs have major limitations to evaluate such complex large-scale combination programs. To close the prevention evaluation gap, alternative evaluation designs are needed, but also better articulation of the program impact pathways and proper documentation of program implementation. Building a plausible case using mixed methods and modeling can provide a valid alternative to probability evidence. HIV prevention policies should not be limited to evidences from randomized designs only.
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