Occupancy numbers for dynamic heterogeneous populations: Estimate of particles lifetimes

A dynamic model of a heterogeneous population is studied. Particles belonging to a population are divided, at every time t, into a finite number of classes according to their types and the partition changes over time. The role of the occupancy numbers, namely the cardinality of each class, is highlighted. The relationship between the stochastic process of occupancy numbers and the process of particle types is analyzed. The main goal of this paper is the estimation of the lifetime of each particle at a given time t, when the observed data are the history of the process of the number of dead particles up to t. Furthermore, a discrete time approximation of the filter is given.     

Predicting Severity of Foreign Body Injuries in Children in Upper Airways: An Approach Based on Regression Trees

The entry of a small item into the upper airways is one of the leading causes of injuries in children up to 14 years old. The aim of this study is to characterize types of objects causing choking along with the features of the children involved in the accident and compare results with current standards. The European Survey on Foreign Bodies Injuries Study (ESFBI) collected data on foreign body injuries from 19 European countries. The data from ESFBI were selected according to the ICD-9-CM codes 933 (foreign body in the pharynx and larynx) and 934 (foreign body in the trachea, bronchi, and lungs). Both a classification tree and a linear discriminant analysis (LDA) have been set up to predict the probability that an injured child experiences a hospitalization. The classification tree provides flowchart-type decision rules and allows for analyzing the impact of the item features, the children characteristics, and the circumstances of the accidents on the severity of the foreign body injuries. Results showed that children younger than 3.5 who are involved in an accident have a high probability to experience a hospitalization.     

Attitudes of Heterosexual Men and Women Toward HIV Negative and Positive Gay Men

Attitudes of Italian heterosexual men and women toward gay men, both HIV positive and negative, are poorly investigated. Italian culture is still extremely conservative and provides limited support to the gay community (e.g., lack of same-sex marriage recognition). Consequently, gay men experience social exclusion and disparities. The present study explores the association between homophobia and closeness with sexual orientation and HIV status. 261 heterosexual Italian men and women were assessed for feelings of closeness and homophobia after reading a vignette where the character was C1: heterosexual and HIV negative; C2: gay and HIV negative; or C3: gay and HIV positive. Experiences of homophobia and closeness varied depending on gender of participant and condition assigned, and higher levels of homophobia were correlated with lower levels of closeness regardless of HIV status. Implications and future directions are discussed.     

AGING,RETIREMENT AND SOCIAL SECURITY IN A MODEL OF INTEREST GROUPS

The assessment of the degree of genetic variation in a natural population, and the nature of that variation, is of central importance in both theoretical and applied population studies. Two “variance” results in population genetics theory are presented. For the first, expressions are found for the expected difference in the estimates of genetic variation in a population obtained by two investigators sampling from the same population in the same generation. The second result concerns the question of whether the degree of genetic variation in a population is best estimated by using the number of alleles observed in a sample of genes or by the number of polymorphic sites observed in the sample. For some combinations of the actual degree of variation and the sample size the former is preferred while for other combinations the latter is preferred. The reason for this is discussed.     

Bayesian sample size determination for phase IIA clinical trials using historical data and semi‐parametric prior's elicitation

The Simon's two‐stage design is the most commonly applied among multi‐stage designs in phase IIA clinical trials. It combines the sample sizes at the two stages in order to minimize either the expected or the maximum sample size. When the uncertainty about pre‐trial beliefs on the expected or desired response rate is high, a Bayesian alternative should be considered since it allows to deal with the entire distribution of the parameter of interest in a more natural way. In this setting, a crucial issue is how to construct a distribution from the available summaries to use as a clinical prior in a Bayesian design. In this work, we explore the Bayesian counterparts of the Simon's two‐stage design based on the predictive version of the single threshold design. This design requires specifying two prior distributions: the analysis prior, which is used to compute the posterior probabilities, and the design prior, which is employed to obtain the prior predictive distribution. While the usual approach is to build beta priors for carrying out a conjugate analysis, we derived both the analysis and the design distributions through linear combinations of B‐splines. The motivating example is the planning of the phase IIA two‐stage trial on anti‐HER2 DNA vaccine in breast cancer, where initial beliefs formed from elicited experts' opinions and historical data showed a high level of uncertainty. In a sample size determination problem, the impact of different priors is evaluated.     

Population constraints on pooled surveys in demographic hazard modeling

In non-experimental research, data on the same population process may be collected simultaneously by more than one instrument. For example, in the present application, two sample surveys and a population birth registration system all collect observations on first births by age and year, while the two surveys additionally collect information on women’s education. To make maximum use of the three data sources, the survey data are pooled and the population data introduced as constraints in a logistic regression equation. Reductions in standard errors about the age and birth-cohort parameters of the regression equation in the order of three-quarters are obtained by introducing the population data as constraints. A halving of the standard errors about the education parameters is achieved by pooling observations from the larger survey dataset with those from the smaller survey. The percentage reduction in the standard errors through imposing population constraints is independent of the total survey sample size.     

Modeling healthcare costs in simultaneous presence of asymmetry,heteroscedasticity and correlation

Highly skewed outcome distributions observed across clusters are common in medical research. The aim of this paper is to understand how regression models widely used for accommodating asymmetry fit clustered data under heteroscedasticity. In a simulation study, we provide evidence on the performance of the Gamma Generalized Linear Mixed Model (GLMM) and log-Linear Mixed-Effect (LME) model under a variety of data-generating mechanisms. Two case studies from health expenditures literature, the cost of strategies after myocardial infarction randomized clinical trial on the cost of strategies after myocardial infarction and the European Pressure Ulcer Advisory Panel hospital prevalence survey of pressure ulcers, are analyzed and discussed. According to simulation results, the log-LME model for a Gamma response can lead to estimations that are biased by as much as 10% of the true value, depending on the error variance. In the Gamma GLMM, the bias never exceeds 1%, regardless of the extent of heteroscedasticity, and the confidence intervals perform as nominally stated under most conditions. The Gamma GLMM with a log link seems to be more robust to both Gamma and log-normal generating mechanisms than the log-LME model.     

Mortality Peaks in Italy in the Late 19th and Early 20th Centuries: Trends by Age and Sex

Infant and youth mortality fell steeply between the Unification of Italy and the 1930s, particularly due to the decline in infectious and parasitic diseases. A number of mortality crises, however, at times halted this decline: the 1908 Calabria-Sicily earthquake, the 1915 Marsica earthquake, World War I and 1918 Spanish INFLUENZA outbreak.This paper focuses as the repercussions of these events on the general survival status of young people from birth to puberty, as well as the main pathologies responsible for the rise in mortality.An analysis of differences in mortality according to sex during the various crises was also performed to ascertain whether the more difficult survival conditions during these periods affected males and females equally.