  Positive continuous outcomes with a point mass at zero are prevalent in
biomedical research. To model the point mass at zero and to provide
marginalized covariate effect estimates, marginalized two part models (MTP)
have been developed for outcomes with lognormal and log skew normal
distributions. In this paper, we propose MTP models for outcomes from a
generalized gamma (GG) family of distributions. In the proposed MTP-GG model,
the conditional mean from a two-part model with a three-parameter GG
distribution is parameterized to provide regression coefficients that have
marginal interpretation. MTP-gamma and MTP-Weibull are developed as special
cases of MTP-GG. We derive marginal covariate effect estimators from each model
and through simulations assess their finite sample operating characteristics in
terms of bias, standard errors, 95% coverage, and rate of convergence. We
illustrate the models using data sets from The Medical Expenditure Survey
(MEPS) and from a randomized trial of addictive disorders and we provide SAS
code for implementation. The simulation results show that when the response
distribution is unknown or mis-specified, which is usually the case in real
data sets, the MTP-GG is preferable over other models.
