Data analysis plan
INTERACTIONS
We established the feasibility of obtaining a sufficient sample of women from the ED [Descriptive statistics were used to evaluate frequency, central tendency, and dispersion of sample characteristics. Generalized linear modeling (GLM) was used to evaluate follow-up measurements of each outcome (willingness to take PrEP, knowledge of PrEP, and readiness to take PrEP) as a function of treatment group, controlling for baseline. The primary outcome, willingness to take PrEP, was measured on a 5-point Likert-type scale from “strongly disagree” [Bayesian statistical inference was used to quantify the chance that an effect of treatment group exists (i.e., the alternative hypothesis) on each outcome, adjusted for baseline. Interactions between treatment and baseline was assessed for knowledge, willingness, and readiness. The Bayesian approach was pre-specified in the protocol to provide an accessible account of the probability of a treatment group difference even in the context of a smaller sample size [Given the observed data and weakly informative priors, Bayesian analyses provide a range of probable estimates called the Decision making in the Bayesian context requires researchers to make judgments based on their subjective interpretation of a meaningful threshold. Heuristics from the literature [