We provide sufficient conditions for estimating from longitudinal data the causal effect of a
time-dependent exposure or treatment on the marginal probability of response for a …

Longitudinal data sets are comprised of repeated observations of an outcome and a set of
covariates for each of many subjects. One objective of statistical analysis is to describe the …

For analyses of longitudinal repeated‐measures data, statistical methods include the
random effects model, fixed effects model and the method of generalized estimating …

The method of generalized estimating equations (GEE) is often used to analyze longitudinal
and other correlated response data, particularly if responses are binary. However, few …

We extend Robins' theory of causal inference for complex longitudinal data to the case of
continuously varying as opposed to discrete covariates and treatments. In particular we …

This article describes a new command, gformula, that is an implementation of the g-
computation procedure. It is used to estimate the causal effect of time-varying exposures on …

Longitudinal studies, where data are repeatedly collected on subjects over a period, are
common in medical research. When estimating the effect of a time‐varying treatment or …

This article discusses extensions of generalized linear models for the analysis of
longitudinal data. Two approaches are considered: subject-specific (SS) models in which …

The growing body of work in the epidemiology literature focused on G-computation includes
theoretical explanations of the method but very few simulations or examples of application …

In observational studies with exposures or treatments that vary over time, standard
approaches for adjustment of confounding are biased when there exist time-dependent …