A two-part random effects model for semicontinuous longitudinal data
Joe Schafer
Penn State University
Refreshments: 3:30 - 4:00 p.m. Friday,
November 12, at 327 Yost
Talk: 4:00 - 5:00 p.m. Friday, November 12, at 327 Yost.
A semicontinuous variable has a portion of responses equal to a single
value (typically zero) and a continuous, often skewed, distribution of
values among the remaining responses. In econometric analyses,
variables of this type have been described by a pair of regression
models, a logistic model for the probability of nonzero response and a
conditional linear model for the mean response given that it is
nonzero. In this paper, we extend two-part regression to
longitudinal settings by introducing random coefficients into both the
logistic and the linear parts. Fitting this two-part random-effects
model poses computational challenges similar to those found in
generalized linear mixed models. Maximum-likelihood estimates for the
fixed coefficients and variance components are found by an approximate
Fisher scoring procedure based on high-order Laplace approximations.
The technique is illustrated with data from the Adolescent Alcohol
Prevention Trial, where we examine reported recent alcohol use for
students from the 7th to the 11th grade and its relationships to
parental monitoring and rebelliousness. This is joint work with
Maren K. Olsen of Penn State.
Questions? Nidhan Choudhuri