One main limitation of LTA is the inability of researchers to ever know the “true” underlying distribution in the population (Bauer and Curran, 2003). The models presented in this study provided a useful representation of the discontinuity in posttreatment drinking, but there is no way of knowing whether this representation is the best way of characterizing the lapse process. For example, nonlinear dynamic systems analysis (Hufford et al., 2003), piecewise LGMM (Colder et al., 2002), repeated measures latent class analyses (Lanza and Collins, 2006), and LGMM with regime switching (Dolan et al., 2005) may provide other means of estimating discontinuous transitions in drinking behavior. Unfortunately, all of these models, as well as LTA, are much more complex than statistical analyses with observed variables. For LTA, in particular, the contingency tables often are large and may contain sparse cells—which can lead to estimation problems.
