A survey of recent developments in the theory and application of composite likelihood is
provided, building on the review paper of Varin (2008). A range of application areas …

Incomplete data are quite common in biomedical and other types of research, especially in
longitudinal studies. During the last three decades, a vast amount of work has been done in …

Hidden Markov models—most often abbreviated to the acronym “HMMs”—are one of the
most successful statistical modelling ideas that have came up in the last forty years: the use …

Missing Data in Clinical Studies provides a comprehensive account of the problems arising
when data from clinical and related studies are incomplete, and presents the reader with …

Meta-analysis is the application of statistics to combine results from multiple studies and
draw appropriate inferences. Its use and importance have exploded over the last 25 years …

Composite marginal likelihoods are pseudolikelihoods constructed by compounding
marginal densities. In several applications, they are convenient surrogates for the ordinary …

Both humanitarian and commercial considerations have spurred intensive search for
methods to reduce the time and cost required to develop new therapies. The identification …

A surrogate endpoint is intended to replace a clinical endpoint for the evaluation of new
treatments when it can be measured more cheaply, more conveniently, more frequently, or …

In many therapeutic areas, the identification and validation of surrogate endpoints is of prime
interest to reduce the duration and/or size of clinical trials. Buyse et al.[Biostatistics 2000; 1 …

A valid surrogate endpoint allows correct inference to be drawn regarding the effect of an
intervention on the unobserved true clinical endpoint of interest. The perceived practical and …