Modelling heterogeneity in Latent Space Models for Multidimensional Networks

Multidimensional network data can have different levels of complexity, as nodes may be characterized by heterogeneous individual-specific features, which may vary across the networks. This paper introduces a class of models for multidimensional network data, where different levels of heterogeneity within and between networks can be considered. The proposed framework is developed in the family of latent space models, and it aims to distinguish symmetric relations between the nodes and node-specific features. Model parameters are estimated via a Markov Chain Monte Carlo algorithm. Simulated data and an application to a real example, on fruits import/export data, are used to illustrate and comment on the performance of the proposed models

A bi-dimensional finite mixture model for longitudinal data subject to dropout

In longitudinal studies, subjects may be lost to follow-up, or miss some of the planned visits, leading to incomplete response sequences. When the probability of non-response, conditional on the available covariates and the observed responses, still depends on unobserved outcomes, the dropout mechanism is said to be non ignorable. A common objective is to build a reliable association structure to account for dependence between the longitudinal and the dropout processes. Starting from the existing literature, we introduce a random coefficient based dropout model where the association between outcomes is modeled through discrete latent effects. These effects are outcome-specific and account for heterogeneity in the univariate profiles. Dependence between profiles is introduced by using a bi-dimensional representation for the corresponding distribution. In this way, we define a flexible latent class structure which allows to efficiently describe both dependence within the two margins of interest and dependence between them. By using this representation we show that, unlike standard (unidimensional) finite mixture models, the non ignorable dropout model properly nests its ignorable counterpart. We detail the proposed modeling approach by analyzing data from a longitudinal study on the dynamics of cognitive functioning in the elderly. Further, the effects of assumptions about non ignorability of the dropout process on model parameter estimates are (locally) investigated using the index of (local) sensitivity to non-ignorability

Hidden Gibbs random fields model selection using Block Likelihood Information Criterion

Performing model selection between Gibbs random fields is a very challenging task. Indeed, due to the Markovian dependence structure, the normalizing constant of the fields cannot be computed using standard analytical or numerical methods. Furthermore, such unobserved fields cannot be integrated out and the likelihood evaluztion is a doubly intractable problem. This forms a central issue to pick the model that best fits an observed data. We introduce a new approximate version of the Bayesian Information Criterion. We partition the lattice into continuous rectangular blocks and we approximate the probability measure of the hidden Gibbs field by the product of some Gibbs distributions over the blocks. On that basis, we estimate the likelihood and derive the Block Likelihood Information Criterion (BLIC) that answers model choice questions such as the selection of the dependency structure or the number of latent states. We study the performances of BLIC for those questions. In addition, we present a comparison with ABC algorithms to point out that the novel criterion offers a better trade-off between time efficiency and reliable results

Zero-inflated regression models for radiation-induced chromosome aberration data: A comparative study

Within the field of cytogenetic biodosimetry, Poisson regression is the classical approach for modeling the number of chromosome aberrations as a function of radiation dose. However, it is common to find data that exhibit overdispersion. In practice, the assumption of equidispersion may be violated due to unobserved heterogeneity in the cell population, which will render the variance of observed aberration counts larger than their mean, and/or the frequency of zero counts greater than expected for the Poisson distribution. This phenomenon is observable for both full- and partial-body exposure, but more pronounced for the latter. In this work, different methodologies for analyzing cytogenetic chromosomal aberrations datasets are compared, with special focus on zero-inflated Poisson and zero-inflated negative binomial models. A score test for testing for zero inflation in Poisson regression models under the identity link is also developed

Residual Site Radiotherapy After Immunochemotherapy in Primary Mediastinal B-Cell Lymphoma: A Monoinstitutional Retrospective Study

Aim: To evaluate the efficacy of residual site radiation therapy (RSRT) on local control (LC), progressionfree (PFS) and overall (OS) survival in patients with primary mediastinal lymphoma (PMBCL), following rituximab and chemotherapy treatment (ICHT). Patients and Methods: The study included 34 patients with PMBCL treated between 2006 and 2014 with ICHT with/without autologous stem cell transplantation and RSRT. Between the end of ICHT/stem cell transplantation and RSRT, patients were evaluated with F-18-fluorodeoxyglucose positron-emission tomography. The gross tumor volume included morphological mediastinal residual disease after ICHT/SCT. The percentage of LC, PFS and OS were assessed. Results: All patients received RSRT with a median dose of 30 Gy. Median follow-up was 82 months. One patient out of 34 (3%) showed progressive disease 9 months from diagnosis. The 10-year PFS and OS were 97% and 97% respectively. Conclusion: RSRT in patients with PMBCL treated with ICHT did not impact unfavorably on LC and patient survival

Minimal Extrathyroidal Extension in Predicting 1-Year Outcomes: A Longitudinal Multicenter Study of Low-to-Intermediate-Risk Papillary Thyroid Carcinoma (ITCO#4)

Background: The role of minimal extrathyroidal extension (mETE) as a risk factor for persistent papillary thyroid carcinoma (PTC) is still debated. The aim of this study was to assess the clinical impact of mETE as a predictor of worse initial treatment response in PTC patients and to verify the impact of radioiodine therapy after surgery in patients with mETE. Methods: We reviewed all records in the Italian Thyroid Cancer Observatory (ITCO) database and selected 2237 consecutive patients with PTC who satisfied the inclusion criteria (PTC with no lymph node metastases and at least 1 year of follow-up). For each case, we considered initial surgery, histological variant of PTC, tumor diameter, recurrence risk class according to the American Thyroid Association (ATA) risk stratification system, use of radioiodine therapy, and initial therapy response, as suggested by ATA guidelines. Results: At 1-year follow-up, 1831 patients (81.8%) had an excellent response, 296 (13.2%) had an indeterminate response, 55 (2.5%) had a biochemical incomplete response, and 55 (2.5%) had a structural incomplete response. Statistical analysis suggested that mETE (odds ratio [OR] 1.16, p=0.65), tumor size >2 cm (OR 1.45, p=0.34), aggressive PTC histology (OR 0.55, p=0.15), and age at diagnosis (OR 0.90, p=0.32) were not significant risk factors for a worse initial therapy response. When evaluating the combination of mETE, tumor size, and aggressive PTC histology, the presence of mETE with a >2 cm tumor was significantly associated with a worse outcome (OR 5.27, 95% CI, p=0.014). The role of radioiodine ablation in patients with mETE was also evaluated. When considering radioiodine treatment, propensity score-based matching was performed, and no significant differences were found between treated and non-treated patients (p=0.24). Conclusions: This study failed to show the prognostic value of mETE in predicting initial therapy response in a large cohort of PTC patients without lymph node metastases. The study suggests that the combination of tumor diameter and mETE can be used as a reliable prognostic factor for persistence and could be easily applied in clinical practice to manage PTC patients with low-to-intermediate risk of recurrent/persistent disease

Semiparametric mixture models for multivariate count data, with application

The analysis of overdispersed counts has been the focus of a wide range of literature, with the general objective of providing reliable parameter estimates in the presence of heterogeneity or dependence among subjects. In this paper we extend the standard variance component models to the analysis of multivariate counts, defining the dependence among counts through a set of correlated random coefficients. Estimation is carried out by numerical integration through an EM algorithm without parametric assumptions upon the random coefficients distribution. The proposed model is computationally parsimonious and, when applied to a real dataset, seems to produce better results than parametric models. A simulation study has been carried out to investigate the behaviour of the proposed models in a series of empirical situation

Two-part regression models for longitudinal zero-inflated count data

Two-part models are quite well established in the economic literature, since they resemble accurately a principal-agent type model, where homogeneous, observable, counted outcomes are subject to a (prior, exogenous) selection choice. The first decision can be represented by a binary choice model, modeled using a probit or a logit link; the second can be analyzed through a truncated discrete distribution such as a truncated Poisson, negative binomial, and so on. Only recently, a particular attention has been devoted to the extension of two-part models to handle longitudinal data. The authors discuss a semi-parametric estimation method for dynamic two-part models and propose a comparison with other, well-established alternatives. Heterogeneity sources that influence the first level decision process, that is, the decision to use a certain service, are assumed to influence also the (truncated) distribution of the positive outcomes. Estimation is carried out through an EM algorithm without parametric assumptions on the random effects distribution. Furthermore, the authors investigate the extension of the finite mixture representation to allow for unobservable transition between components in each of these parts. The proposed models are discussed using empirical as well as simulated data