Several authors have proposed the use of multilevel models to analyze data from single-case designs. This article extends that work in 2 ways. First, examples are given of how to estimate these models when the single-case designs have features that have not been considered by past authors. These include the use of polynomial coefficients to model nonlinear change, the modeling of counts (Poisson distributed) or proportions (binomially distributed) as outcomes, the use of 2 different ways of modeling treatment effects in ABAB designs, and applications of these models to alternating treatment and changing criterion designs. Second, issues that arise when multilevel models are used for the analysis of single-case designs are discussed; such issues can form part of an agenda for future research on this topic. These include statistical power and assumptions, applications to more complex single-case designs, the role of exploratory data analyses, extensions to other kinds of outcome variables and sampling distributions, and other statistical programs that can be used to do such analyses.