Statistical Modelling 4 (2004), 324–338
Regression models for pretest/posttest data in blocks
Julio M. Singer, Juvênico S. Nobre and Henry C. Sef
Departamento de Estatística,
Universidade de São Paulo,
Caixa Postal 66281,
São Paulo,
Brazil
eMail:
jmsinger@ime.usp.br
Abstract:
We consider regression models with no intercepts to analyse
pretest/posttest data from a dental study conducted under an
experimental design involving a blocked factorial structure
with two within individual factors. The proposed models
accommodate block effects, heteroscedasticity, nonlinear
relations between pretest and posttest measures and repeated
measures. We compare multiplicative lognormal and gamma models
to additive normal models fitted via generalized linear models
methodology for repeated measures. Alternatively, we consider
standard linear mixed models methodology to fit lognormal
models, an option that facilitates modelling the within
subjects covariance structure.
Keywords:
generalized linear models; pretest/posttest experiments;
regression models; repeated measures
Downloads:
Data
in zipped archive
back