Graphical models are multivariate statistical models where conditional independence relations among random variables are represented by the missing edges of a graph whose nodes are the random variables. When the graph used to represent the model is a directed acyclic graph (DAG) these models are also useful to represent causal relations. This causal interpretation makes these models useful in areas such as genomics, psychology, and epidemiology. When the random variables under consideration are all discrete, it is useful, for modelling purposes, to consider a more general form of conditional independence called context-specific independence. Encoding context-specific independence using graphical models is an interesting challenge which has been considered previously by Heckerman (1990), Geiger and Heckerman (1996), Boutelier et al (1996), Smith and Anderson (2008), and Pensar et al 2015. The goal of this talk is to present a new way of representing context-specific causal models. We prove that these models generalize several important properties of graphical models and present a way to model interventions in these models. This is joint work with Liam Solus (KTH, Sweden).

In this seminar, Carlo Giovanni Camarada presents a work co-authored with Maria Durbán that proposes a clear-cut and fast method to obtain coherent cause-specific mortality trajectories based on Lagrange multipliers. The authors apply the method proposed to fit and forecast the mortality of males in the USA for the five leading causes of death.

Rosina Savisaar, is a Statistics educator and consultant, founder of Mondego Science, a Teaching and Consultancy company specialized in Statistics and Data Analysis. In this talk, Rosina will share her thoughts on Bayesian Statistics.

Several joint models for longitudinal and survival data have been proposed in recent years. In particular, many authors have preferred to employ the Bayesian approach to model more complex structures, make dynamic predictions, or use model averaging. However, Markov chain Monte Carlo methods are computationally very demanding and may suffer convergence problems, especially for complex models with random effects, which is the case for most joint models. These issues can be overcome by estimating the parameters of each submodel separately, leading to a natural reduction in the complexity of the joint modeling, but often producing biased estimates. Hence, we propose a novel two-stage approach that uses the estimations from the longitudinal submodel to specify an informative prior distribution for the random effects when estimating them within the survival submodel. In addition, as a bias correction mechanism, we incorporate the longitudinal likelihood function in the second stage, where its fixed effects are set according to the estimation using only the longitudinal submodel. Based on simulation studies and real applications, we empirically compare our proposal with joint specification and standard two-stage approaches considering different types of longitudinal responses (continuous, count, and binary) that share information with a Weibull proportional hazard model. The results show that our estimator is more accurate than its two-stage competitor and as good as jointly estimating all parameters. Moreover, the novel two-stage approach significantly reduces the computational time compared to the joint specification.

Biostatisticians nowadays can choose from a huge toolbox of advanced methods and algorithms for prediction purposes. Some of these tools are based on concepts from machine learning; other methods rely on more classical statistical modeling approaches. In clinical settings, doctors are sometimes reluctant to consider risk scores that are constructed by black-box algorithms without clinically meaningful interpretation. Furthermore, even both an accurate and interpretable model will not often be used in practice, when it is based on variables that are difficult to obtain in clinical routine or when its calculation is too complex.

In this talk, I will give a non-technical introduction to statistical boosting algorithms which can be interpreted as the methodological intersection between machine learning and statistical modeling. Boosting is able to perform variable selection while estimating statistical models from potentially high-dimensional data. It is mainly suitable for exploratory data analysis or prediction purposes. I will give an overview of some current methodological developments (including the development of polygenic scores) and provide an example of the construction of a clinical risk score with surprisingly simple solutions.