## John Carlin, University of Melbourne

### Choice and Interpretation of Multilevel Models for Longitudinal
Binary Outcome Data: A Case Study on Adolescent Smoking

Building
statistical models to address questions about change over time in
binary or ordinal outcomes is substantially more difficult than for
continuous normally distributed outcomes. A large literature has grown
up on the use of multilevel or hierarchical models for normal
outcomes. Similar models are gaining increasing use for binary
outcomes, but a number of questions remain about their effective
application. For example, is it realistic to assume that heterogeneity
between subjects can be adequately represented by a normally
distributed random intercept (and/or slope)? What implications does
this assumption have for substantive conclusions that may be drawn
from estimated model parameters, and to what extent can the assumption
be checked from the data? I will illustrate and discuss these
questions in the context of a specific problem concerning smoking
behavior in a longitudinal study of adolescent health, by comparing
the results of fitting a standard logistic-normal model with those of
fitting an alternative mixture model.