The package allows to estimate the intervention effect in cluster randomized trials (CRTs). It implements a semi-parametric GEE estimator accounting for missing data with Inverse probability weighting (IPW) and for imbalance in covariates with augmentation (AUG). The estimator IPW-AUG-GEE is Doubly robust (DR). Importantly, We exhibited that in CRTs most of the available softwares use an implementation of weights that lead to a bias in estimation if a non-independence working correlation structure is chosen. In CRTgeeDR, we solve this problem by using a different implementation of weighting while keeping the consistency properties of the IPW (and thus if the DR).

Models based on ordinary differential equations (ODE) are a widespread tool to describe dynamical system. In biomedical sciences, data within each subject can be sparse but information is often gained from between-subjects variability. This makes natural the use of mixed effect models to estimate population parameters. Although maximum likelihood based approaches are a valuable option, both numerical and identifiability issues favor a Bayesian approach which can incorporate prior knowledge in a flexible way. However the combination of difficulties coming from the ODE system and from the presence of random effects raises a major numerical challenge. A normal approximation of the posterior can be obtained by computing the maximum of the posterior distribution (MAP).

A dedicated website to this program is available, it contains documentation and instruction of use and enables source code download.

This algorithm provides a numerical solution to the problem of minimizing a function. This is more efficient than the Gauss-Newton-like algorithm when starting from points very far from the final minimum. A new convergence test is implemented (RDM) in addition to the usual stopping criterion : stopping rule is when the gradients are small enough in the parameters metric (GH-1G).