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Bayesian hierarchical models and recent computational development using Integrated Nested Laplace Approximation, with applications to pre-implantation genetic screening in IVF

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If you have a question about this talk, please contact Dr Jack Bowden.

Bayesian hierarchical models are an effective way of accounting for complex structures in the data, including clustering and nested levels of information. Typically, within a Bayesian framework, hierarchical models are estimated using Markov Chain Monte Carlo methods. These are standard in Bayesian analysis but, while generally very efficient, they can be extremely computationally intensive, especially for hierarchical models. Recently, alternative methods have been investigated to increase the computational efficiency and the precision in the estimations. In this talk, I review the theory behind Integrated Nested Laplace Approximation; in particular, I show an example based on data obtained at the UCL Centre for Pre-implantation Genetic Diagnosis, investigating the effect of the length of telomeres (a 6 base repeat found at the end of chromosomes to protect them from degradation) on chromosomal abnormalities in IVF .

This talk is part of the MRC Biostatistics Unit Seminars series.

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