This paper proposes the percentile curves concept as conditional probabilities curves across representative percentiles of the distribution of curves induced by random effects in a logistic model with random intercepts. The authors extend this concept to a logistic model with random intercepts and slopes and propose a methodology to approximate the percentile curves using the Monte-Carlo technique. The authors apply this concept to a binary longitudinal data set. The results suggest that the percentile curves complement the analysis of longitudinal data and permit a marginal interpretation of subject-specific parameters.
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