On the use of extreme value tail modeling for generalized pairwise comparisons with censored outcomes

In randomized clinical trials, methods of pairwise comparisons such as the ‘Net Benefit’ or the ‘win ratio’ have recently gained much attention when interests lies in assessing the effect of a treatment as compared to a standard of care. Among other advantages, these methods are usually praised for delivering a treatment measure that can easily handle multiple outcomes of different nature, while keeping a meaningful interpretation for patients and clinicians. For time-to-event outcomes, a recent suggestion emerged in the literature for estimating these treatment measures by providing a natural handling of censored outcomes. However, this estimation procedure may lead to biased estimates when tails of survival functions cannot be reliably estimated using Kaplan–Meier estimators. The problem then extrapolates to the other outcomes incorporated in the pairwise comparison construction. In this work, we suggest to extend the procedure by the consideration of a hybrid survival function estimator that relies on an extreme value tail model through the Generalized Pareto distribution. We provide an estimator of treatment effect measures that notably improves on bias and remains easily apprehended for practical implementation. This is illustrated in an extensive simulation study as well as in an actual trial of a new cancer immunotherapy.     

Urban risks due to climate change in the Andean municipality of Pasto,Colombia: A Bayesian network approach

The current trends of climate change will increase people's exposure to urban risks related to events such as landslides, floods, forest fires, food production, health, and water availability, which are stochastic and very localized in nature. This research uses a Bayesian network (BN) approach to analyze the intensity of such urban risks for the Andean municipality of Pasto, Colombia, under climate change scenarios. The stochastic BN model is linked to correlational models and local scenarios of representative concentration trajectories (RCP) to project the possible risks to which the municipality of Pasto will be exposed in the future. The results show significant risks in crop yields, food security, water availability and disaster risks, but no significant risks on the incidence of acute diarrheal diseases (ADD) and acute respiratory infections (ARI), whereas positive outcomes are likely to occur in livestock production, influenced by population growth. The advantage of the BN approach is the possibility of updating beliefs in the probabilities of occurrence of events, especially in developing, intermediate cities with information-limited contexts.     

Connecting Two Sides: A Qualitative Study on Social Innovation Ventures and Poor Communities in an Emerging Economy

VOLUNTAS: International Journal of Voluntary and Nonprofit Organizations - The relationship between social innovation venture and poor communities has received little attention from studies in the...     

The Moderating Effect of Countries’ Development on the Characterization of the Social Entrepreneur: An Empirical Analysis with GEM Data

VOLUNTAS: International Journal of Voluntary and Nonprofit Organizations - The objective of this paper is to analyze the moderating effect that the level of development of countries exerts on the...     

Social Capital and Community Heterogeneity

Recent findings indicate that more pronounced community heterogeneity is associated with lower levels of social capital. These studies, however, concentrate on specific aspects in which people differ (such as income inequality or ethnic diversity). In the present paper, we introduce the number of parties in the local party system as a more encompassing measure of community heterogeneity. This builds on the argument that the number of relevant socio-economic cleavages in the population (i.e. heterogeneity) determines the level of party system fragmentation. Using data on 307 Flemish municipalities, we find that municipalities with a more heterogeneous population indeed have lower levels of social capital. Hence, our study endorses—and generalizes—previous results linking community heterogeneity to lower levels of social capital.

Detecting level shifts in ARMA-GARCH (1,1) Models

The purpose of this article is to present a new method to detect level shifts in the context of conditional heteroscedastic models. First, we define precisely what type of outlier we are referring to, a concept that has been scarcely touched in the field of GARCH (1,1) models, and then we go on to present our methodology based on the nature of the Lagrange multiplier tests. The validity and efficiency of the proposed procedure are demonstrated through different simulation experiments. To conclude, we present a practical application of the method to the time series of returns of US short-term interest rates.     

Resampling schemes with low resampling intensity and their applications in testing hypotheses

The paper explores statistical features of different resampling schemes under low resampling intensity. The original sample is considered in a very general framework of triangular arrays, without independence or equally distributed assumptions, although improvements under such conditions are also provided. We show that low resampling schemes have very interesting and flexible properties, providing new insights into the performance of widely used resampling methods, including subsampling, two-sample unbalanced permutation statistics or wild bootstrap. It is shown that, under regularity assumptions, resampling tests with critical values derived by the appertaining low resampling procedures are asymptotically valid and there is no loss of power compared with the power function of an ideal (but unfeasible) parametric family of tests. Moreover we show that in several contexts, including regression models, they may act as a filter for the normal part of a limit distribution, turning down the influence of outliers.     

Testing interaction in some predator–prey populations

This paper deals with a testing problem for each of the interaction parameters of the Lotka–Volterra ordinary differential equations system~(ODE). In short, when the rates of birth and death are fixed, we would like to test if each interaction parameter is higher or lower than a fixed reference rate. We choose a statistical model where the actual population sizes are modelled as random perturbations of the solutions to this ODE. By assuming that the random perturbations follow correlated Ornstein–Uhlenbeck processes, we propose the uniformly most powerful test concerning each interaction parameter of the ODE and, we establish the asymptotic properties of the test. Further, we illustrate the suggested test on the Canadian mink–muskrat data set. This research has received the financial support from Natural Sciences and Engineering Research Council of Canada and Institut des Sciences Mathématiques.     

Hierarchical Bayesian meta-analysis models for cross-platform microarray studies

The development of new technologies to measure gene expression has been calling for statistical methods to integrate findings across multiple-platform studies. A common goal of microarray analysis is to identify genes with differential expression between two conditions, such as treatment versus control. Here, we introduce a hierarchical Bayesian meta-analysis model to pool gene expression studies from different microarray platforms: spotted DNA arrays and short oligonucleotide arrays. The studies have different array design layouts, each with multiple sources of data replication, including repeated experiments, slides and probes. Our model produces the gene-specific posterior probability of differential expression, which is the basis for inference. In simulations combining two and five independent studies, our meta-analysis model outperformed separate analyses for three commonly used comparison measures; it also showed improved receiver operating characteristic curves. When combining spotted DNA and CombiMatrix short oligonucleotide array studies of Geobacter sulfurreducens, our meta-analysis model discovered more genes for fixed thresholds of posterior probability of differential expression and Bayesian false discovery than individual study analyses. We also examine an alternative model and compare models using the deviance information criterion.