Pairwise fitting of piecewise mixed models for the joint modeling of multivariate longitudinal outcomes, in a randomized crossover trial.

Many statistical models have been proposed in the literature for the analysis of longitudinal data. One may propose to model two or more correlated longitudinal processes simultaneously, with a goal of understanding their association over time. Joint modeling is then required to carefully study the association structure among the outcomes as well as drawing joint inferences about the different outcomes.

A reflection on the causal interpretation of individual-level surrogacy.

At the beginning of the 21st century, a new paradigm was introduced for the evaluation of surrogate endpoints based on meta-analysis. In this paradigm, the putative surrogate is assessed at two different levels, the so-called, trial and individual level. Trial level surrogacy is defined as the association between the expected causal treatment effects across different trials populations, whereas the individual level is defined as the association between the surrogate and true endpoints, after adjusting by trial and treatment.

A closed-form estimator for meta-analysis and surrogate markers evaluation.

Estimating complex linear mixed models using an iterative full maximum likelihood estimator can be cumbersome in some cases. With small and unbalanced datasets, convergence problems are common. Also, for large datasets, iterative procedures can be computationally prohibitive.