Showing papers by "Francesco Bartolucci published in 2018"

GDP dynamics and unemployment changes in developed and developing countries

Abstract: We design a new specification of Okun’s model that takes the following features into account: estimation of the relation in first differences, the possible lagged effect of GDP dynamics on ...

Bedside sonography assessment of extravascular lung water increase after major pulmonary resection in non-small cell lung cancer patients.

Abstract: Background: Extra vascular lung water (EVLW) following pulmonary resection increases due to fluid infusion and rises in capillary surface and permeability of the alveolar capillary membranes. EVLW increase clinically correlates to pulmonary oedema and it may generate impairments of gas exchanges and acute lung injury. An early and reliable assessment of postoperative EVLW, especially following major pulmonary resection, is useful in terms of reducing the risk of postoperative complications. The currently used methods, though satisfying these criteria, tend to be invasive and cumbersome and these factors might limit its use. The presence and burden of EVLW has been reported to correlate with sonographic B-line artefacts (BLA) assessed by lung ultrasound (LUS). This observational study investigated if bedside LUS could detect EVLW increases after major pulmonary resection. Due to the clinical association between EVLW increase and impairment of gas exchange, secondary aims of the study included investigating for associations between any observed EVLW increases and both respiratory ratio (PaO2/FiO2) and fluid retention, measured by brain natriuretic peptide (BNP). Methods: Overall, 74 major pulmonary resection patients underwent bedside LUS before surgery and at postoperative days 1 and 4, in the inviolate hemithorax which were divided into four quadrants. BLA were counted with a four-level method. The respiratory ratio PaO2/FiO2 and fluid retention were both assessed. Results: BLA resulted being increased at postoperative day 1 (OR 9.25; 95% CI, 5.28–16.20; P<0.0001 vs. baseline), and decreased at day 4 (OR 0.50; 95% CI, 0.31–0.80; P=0.004 vs. day 1). Moreover, the BLA increase was associated with both increased BNP (OR 1.005; 95% CI, 1.003–1.008; P<0.0001) and body weight (OR 1.040; 95% CI, 1.008–1.073; P=0.015). Significant inverse correlations were observed between the BLA values and the PaO 2 /FiO 2 respiratory ratios. Conclusions: Our results suggest that LUS, due to its non-invasiveness, affordability and capacity to detect increases in EVLW, might be useful in better managing postoperative patients.

Latent Ignorability and Item Selection for Nursing Home Case-Mix Evaluation

Abstract: In the social, behavioral, and health sciences it is often of interest to identify latent or unobserved groups in the population with the group membership of the individuals depending on a set of observed variables. In particular, we focus on the field of nursing home assessment in which the response variables typically come from the administration of questionnaires made of categorical items. These types of data may suffer from missing values and the use of lengthy questionnaires may be problematic as a large number of items could have a negative impact on the responses. In such a context, we introduce an extended version of the Latent Class (LC) model aimed at dealing with missing values, by assuming a form of latent ignorability. Moreover, we propose an item selection algorithm, based on the LC model, for finding the smallest subset of items providing an amount of information close to that of the initial set. The proposed approach is illustrated through an application to a dataset collected within an Italian project on the quality-of-life of nursing home patients.

A joint model for longitudinal and survival data based on an AR(1) latent process

Abstract: A critical problem in repeated measurement studies is the occurrence of nonignorable missing observations. A common approach to deal with this problem is joint modeling the longitudinal and survival processes for each individual on the basis of a random effect that is usually assumed to be time constant. We relax this hypothesis by introducing time-varying subject-specific random effects that follow a first-order autoregressive process, AR(1). We also adopt a generalized linear model formulation to accommodate for different types of longitudinal response (i.e. continuous, binary, count) and we consider some extended cases, such as counts with excess of zeros and multivariate outcomes at each time occasion. Estimation of the parameters of the resulting joint model is based on the maximization of the likelihood computed by a recursion developed in the hidden Markov literature. This maximization is performed on the basis of a quasi-Newton algorithm that also provides the information matrix and then standard ...

Testing for State Dependence in Binary Panel Data with Individual Covariates by a Modified Quadratic Exponential Model

Abstract: We propose a test for state dependence in binary panel data with individual covariates. For this aim, we rely on a quadratic exponential model in which the association between the response variables is accounted for in a different way with respect to more standard formulations. The level of association is measured by a single parameter that may be estimated by a Conditional Maximum Likelihood (CML) approach. Under the dynamic logit model, the conditional estimator of this parameter converges to zero when the hypothesis of absence of state dependence is true. Therefore, it is possible to implement a t-test for this hypothesis which may be very simply performed and attains the nominal significance level under several structures of the individual covariates. Through an extensive simulation study, we find that our test has good finite sample properties and it is more robust to the presence of (autocorrelated) covariates in the model specification in comparison with other existing testing procedures for state ...

A multilevel latent Markov model for the evaluation of nursing homes' performance.

Abstract: The periodic evaluation of health care services is a primary concern for many institutions. In this work, we focus on nursing home services with the aim to produce a ranking of a set of nursing homes based on their capability to improve - or at least to keep unchanged - the health status of the patients they host. As the overall health status is not directly observable, latent variable models represent a suitable approach. Moreover, given the longitudinal and multilevel structure of the available data, we rely on a multilevel latent Markov model where patients and nursing homes are the first and the second level units, respectively. The model includes individual covariates to account for the patient case-mix and the impact of nursing home membership is modeled through a pair of correlated random effects affecting the initial distribution and the transition probabilities between different levels of health status. Through the prediction of these random effects we obtain a ranking of the nursing homes. Furthermore, the proposed model is designed to address non-ignorable dropout, which typically occurs in these contexts because some elderly patients die before completing the survey. We apply our model to the Long Term Care Facilities dataset, a longitudinal dataset gathered from Regione Umbria (Italy). Our results are robust to the sensitivity parameter involved (the number of latent states) and show that differences in nursing homes' performances are statistically significant. The authors certify that they have the right to deposit this contribution in its published format with MPRA.

Marginal models with individual-specific effects for the analysis of longitudinal bipartite networks

Abstract: A new modeling framework for bipartite social networks arising from a sequence of partially time-ordered relational events is proposed. We directly model the joint distribution of the binary variables indicating if each single actor is involved or not in an event. The adopted parametrization is based on first- and second-order effects, formulated as in marginal models for categorical data and free higher order effects. In particular, second-order effects are log-odds ratios with meaningful interpretation from the social perspective in terms of tendency to cooperate, in contrast to first-order effects interpreted in terms of tendency of each single actor to participate in an event. These effects are parametrized on the basis of the event times, so that suitable latent trajectories of individual behaviors may be represented. Inference is based on a composite likelihood function, maximized by an algorithm with numerical complexity proportional to the square of the number of units in the network. A classification composite likelihood is used to cluster the actors, simplifying the interpretation of the data structure. The proposed approach is illustrated on a dataset of scientific articles published in four top statistical journals from 2003 to 2012.

A generalized moving average convergence/divergence for testing semi-strong market efficiency

Abstract: We propose a generalized version of the moving average converge divergence (MACD) indicator widely employed in the technical analysis and trading of financial markets. By assuming a martingale model with drift for prices, as well as for their transformed values, we propose a test statistic for the local drift and derive its main theoretical properties. The semi-strong market efficiency hypothesis is assessed through a bootstrap test. We conclude by applying the indicator to monitor the crude oil prices over a 6 years period.

A longitudinal analysis of the degree of accomplishment of anti-corruption measures by Italian municipalities: a latent Markov approach.

Abstract: The recent Italian anti-corruption law has introduced a new figure, the supervisor for corruption prevention, who has to fill in an annual report about the accomplishment of anti-corruption measures within the institution he/she represents. Using data coming from such annual reports referred to a sample of Italian municipalities, a latent Markov model is fitted to investigate the evolution over time of the degree of accomplishment of anti-corruption measures. First results evidence three latent states of increasing virtuosity. Moreover, at the beginning of the study, the most of the sample belongs to the low and intermediate states of virtuosity, even if there is evidence of high probabilities to move to upper states over time.

Causality patterns of a marketing campaign conducted over time: evidence from the latent Markov model

Abstract: Many statistical methods currently employed to evaluate the effect of a marketing campaign in dealing with observational data advocate strong parametric assumptions to correct for endogeneity among the participants. In addition, the assumptions compromise the estimated values when applied to data in which the research expects endogeneity but this is not realized. Based on the recent advances in the literature of causal models dealing with data collected across time, we propose a dynamic version of the inverse-probability-of-treatment weighting within the latent Markov model. The proposal, which is based on a weighted maximum likelihood approach, accounts for endogeneity without imposing strong restrictions. The likelihood function is maximized through the Expectation-Maximization algorithm which is suitably modified to account for the inverse probability weights. Standard errors for the parameters estimates are obtained by a nonparametric bootstrap method. We show the effects of multiple mail campaigns conducted by a large European bank with the purpose to influence their customers to the acquisitions of the addressed financial products.

A joint model for longitudinal and survival data based on a continuous-time hidden Markov model

Abstract: A shared-parameter approach for jointly modelling longitudinal and survival data is proposed. With respect to available approaches, it allows for time-varying random effects that affect both the longitudinal and the survival processes. The distribution of these random effects is modelled according to a continuous-time hidden Markov chain, so that latent transitions may occur at any time point. Our formulation allows for (i) informative drop-out with precise time-to-event outcomes, while existing approaches are all based on drop-out indicators at precise measurement times, a feature that is at the least discarding possibly valuable information and (ii) completely non-parametric treatment of unequally spaced intervals between consecutive measurement occasions (even not in the presence of drop-out). For maximum likelihood estimation we propose an algorithm based on coarsening. The resulting estimator is studied by simulation. The approach is illustrated by an application to data about patients suffering from mildly dilated cardiomyopathy.