Browsing by Author "Guglielmi, Alessandra"

Now showing 1 - 5 of 5
  • Thumbnail Image
    Item
    A semiparametric Bayesian joint model for multiple mixed-type outcomes: an application to acute myocardial infarction
    (2018) Guglielmi, Alessandra; Ieva, Francesca; Paganoni, Anna Maria; Quintana Quintana, Fernando
  • Thumbnail Image
    Item
    A Simple Class of Bayesian Nonparametric Autoregression Models
    (2013) Di Lucca, Maria Anna; Guglielmi, Alessandra; Müller, Peter; Quintana Quintana, Fernando
  • Thumbnail Image
    Item
    Bayesian first order auto-regressive latent variable models for multiple binary sequences
    (SAGE PUBLICATIONS LTD, 2011) Giardina, Federica; Guglielmi, Alessandra; Quintana, Fernando A.; Ruggeri, Fabrizio
    Longitudinal clinical trials often collect long sequences of binary data monitoring a disease process over time. Our application is a medical study conducted in the US by the Veterans Administration Cooperative Urological Research Group to assess the effectiveness of a chemotherapy treatment (thiotepa) in preventing recurrence on subjects affected by bladder cancer. We propose a generalized linear model with latent auto-regressive structure for longitudinal binary data following a Bayesian approach. We discuss inference as well as sensitivity to prior choices for the bladder cancer data. We find that there is a significant treatment effect in the sense that treated patients have much smaller predicted recurrence probabilities than placebo patients.
  • Thumbnail Image
    Item
    Childhood obesity in Singapore: A Bayesian nonparametric approach
    (2024) Beraha, Mario; Guglielmi, Alessandra; Quintana, Fernando Andres; De Iorio, Maria; Eriksson, Johan Gunnar; Yap, Fabian
    Overweight and obesity in adults are known to be associated with increased risk of metabolic and cardiovascular diseases. Obesity has now reached epidemic proportions, increasingly affecting children. Therefore, it is important to understand if this condition persists from early life to childhood and if different patterns can be detected to inform intervention policies. Our motivating application is a study of temporal patterns of obesity in children from South Eastern Asia. Our main focus is on clustering obesity patterns after adjusting for the effect of baseline information. Specifically, we consider a joint model for height and weight over time. Measurements are taken every six months from birth. To allow for data-driven clustering of trajectories, we assume a vector autoregressive sampling model with a dependent logit stick-breaking prior. Simulation studies show good performance of the proposed model to capture overall growth patterns, as compared to other alternatives. We also fit the model to the motivating dataset, and discuss the results, in particular highlighting cluster differences. We have found four large clusters, corresponding to children sub-groups, though two of them are similar in terms of both height and weight at each time point. We provide interpretation of these clusters in terms of combinations of predictors.
  • Thumbnail Image
    Item
    The Semi-Hierarchical Dirichlet Process and Its Application to Clustering Homogeneous Distributions
    (INT SOC BAYESIAN ANALYSIS, 2021) Beraha, Mario; Guglielmi, Alessandra; Quintana, Fernando A.
    Assessing homogeneity of distributions is an old problem that has received considerable attention, especially in the nonparametric Bayesian literature. To this effect, we propose the semi-hierarchical Dirichlet process, a novel hierarchical prior that extends the hierarchical Dirichlet process of Teh et al. (2006) and that avoids the degeneracy issues of nested processes recently described by Camerlenghi et al. (2019a). We go beyond the simple yes/no answer to the homogeneity question and embed the proposed prior in a random partition model; this procedure allows us to give a more comprehensive response to the above question and in fact find groups of populations that are internally homogeneous when I >= 2 such populations are considered. We study theoretical properties of the semi hierarchical Dirichlet process and of the Bayes factor for the homogeneity test when I = 2. Extensive simulation studies and applications to educational data are also discussed.