gllamm is a program to fit generalised linear latent and mixed models. Since gllamm6 appeared in the STB (sg129), a large number of new features have been added. Two important extensions will be discussed: 1) More response processes can now be modelled including ordered and unordered categorical...

gllamm can estimate both conventional and unconventional latent class models. Models are specified using discrete latent variables whose values determine the conditional response distributions for the classes. A new feature of gllamm is that latent class probabilities can depend on covariates....

This presentation focuses on predicted probabilities for multilevel models for dichotomous or ordinal responses. In a three-level model, for instance with patients nested in doctors nested in hospitals, predictions for patients could be for new or existing doctors and, in the latter case, for...

Generalized linear mixed models are generalized linear models that include random effects varying between clusters or 'higher-level' units of hierarchically structured data. Such models can be estimated using gllamm. The prediction command gllapred can be used to obtain empirical Bayes...

The gllamm procedure provides a framework in which to undertake many of the more difficult analyses required for trials and intervention studies. Treatment effect estimation in the presence of noncompliance can be undertaken using instrumental variable (IV) methods. I illustrate how gllamm can...

-gllamm- provides a framework within which many of the more difficult analyses required for trials and intervention studies may be undertaken. Treatment effect estimation in the presence of non-compliance can be undertaken using instrumental variable (IV) methods. We illustrate how -gllamm- can...

We describe Stata macros that implement the composite link approach to missing data in log-linear models first described by David Rindskopf (Psychometrika, 1992, V57, 29-42). When a missing value occurs among the variables that form a contingency table, the resulting observation contributes to...

Ordered categorical responses can be analyzed with different kinds of logistic regression models, the most popular being the cumulative logit or proportional odds model. Alternatively, ordinal probit models can be specified. When the data have a nested structure, with repeated observations for...

Models for handling sample selection or informative missingness have been developed for both cross sectional and longitudinal or panel data. For cross sectional data, Heckman (1979) suggested a joint model for the response and sample selection processes where the disturbances of the processes...

This presentation focuses on predicted probabilities for multilevel models for dichotomous or ordinal responses. In a three-level model, for instance, with patients nested in doctors nested in hospitals, predictions for patients could be for new or existing doctors and, in the latter case, for...