Ordinal data: a new model with applications

In this work we propose a new discrete probability distribution useful when we work with ordered categorical data. The discrete-beta distribution has a highly flexible shape and it can be either over-dispersed or under-dispersed with respect to the binomial distribution. It has only two parameters, which have a very clear interpretation: the mean and the precision parameters of the beta latent variable. Adding directly covariates on parameters (according to CUB model framework), it is very suitable to regression

A new parsimonious model for ordinal longitudinal data with application to subjective evaluations of a gastrointestinal disease

In this paper, a new discrete statistical model for ordered categorical data is proposed via fixed-point discretization of a beta latent variable. The resulting discretized beta distribution has a highly flexible shape and it can be either over-dispersed or under-dispersed with respect to the binomial distribution. It has only two parameters, which may therefore parsimoniously depend on covariates and on random effects, providing new tools for the analysis of structured, clustered or longitudinal ordinal data. Practical examples and advices are given and an application of the new model to subjective evaluations of a gastrointestinal disease is shown

A straightforward meta-analysis approach for oncology phase I dose-finding studies

Phase I early-phase clinical studies aim at investigating the safety and the underlying dose-toxicity relationship of a drug or combination. While little may still be known about the compound's properties, it is crucial to consider quantitative information available from any studies that may have been conducted previously on the same drug. A meta-analytic approach has the advantages of being able to properly account for between-study heterogeneity, and it may be readily extended to prediction or shrinkage applications. Here we propose a simple and robust two-stage approach for the estimation of maximum tolerated dose(s) (MTDs) utilizing penalized logistic regression and Bayesian random-effects meta-analysis methodology. Implementation is facilitated using standard R packages. The properties of the proposed methods are investigated in Monte-Carlo simulations. The investigations are motivated and illustrated by two examples from oncology.Comment: 30 pages, 11 figures, 8 table

A proximity-based method to identify genomic regions correlated with a continuously varying environmental variable

Knowledge of markers in the human genome which show spatial patterns and display extreme correlation with different environmental determinants play an important role in understanding the factors which affect the biological evolution of our species. We used the genotype data of more than half a million single nucleotide polymorphisms (SNPs) from the data set Human Genome Diversity Panel (HGDP-CEPH -CEPH) and we calculated Spearman's correlation between absolute latitude and one of the two allele frequen- cies of each SNP. We selected SNPs with a correlation coefficient within the upper 1% tail of the distribution. We then used a criterion of proximity between significant variants to focus on DNA regions showing a continuous signal over a portion of the genome. Based on external information and genome annotations, we demonstrated that most regions with the strongest signals also have biological relevance. We believe this proximity requirement adds an edge to our novel method compared to the existing literature, highlighting several genes (for example DTNB, DOT1L, TPCN2, RELN, MSRA, NRG3) related to body size or shape, human height, hair color, and schizophrenia. Our approach can be applied generally to any measure of association between polymorphic frequencies and continuously varying environmental variable

Using dichotomized survival data to construct a prior distribution for a Bayesian seamless Phase II/III clinical trial

Master protocol designs allow for simultaneous comparison of multiple treatments or disease subgroups. Master protocols can also be designed as seamless studies, in which two or more clinical phases are considered within the same trial. They can be divided into two categories: operationally seamless, in which the two phases are separated into two independent studies, and inferentially seamless, in which the interim analysis is considered an adaptation of the study. Bayesian designs are scarcely studied. Our aim is to propose and compare Bayesian operationally seamless Phase II/III designs using a binary endpoint for the first stage and a time-to-event endpoint for the second stage. At the end of Phase II, arm selection is based on posterior (futility) and predictive (selection) probabilities. The results of the first phase are then incorporated into prior distributions of a time-to-event model. Simulation studies showed that Bayesian operationally seamless designs can approach the inferentially seamless counterpart, allowing for an increasing simulated power with respect to the operationally frequentist design

Fertilización potásica de los principales cultivos bajo siembra directa mecanizada en la región oriental del Paraguay

El uso continuo de los suelos y nuevas variedades con alto potencial de rendimiento hace necesario aplicar fertilización potásica en sistemas agrícolas manejados en siembra directa en la región oriental de Paraguay. Los objetivos de esta investigación fueron evaluar el efecto de la fertilización potásica sobre el rendimiento de trigo, maíz y soja, y determinar el nivel crítico de potasio en el suelo. Se condujeron seis experimentos en fincas de agricultores de Caaguazú, San Pedro y Amambay. El diseño experimental fue bloques completamente al azar con tres repeticiones. Los tratamientos fueron 0, 25, 50, 75 y 100 kg K2O ha-1, las aplicaciones se realizaron con cloruro de potasio al voleo al momento de la siembra de los cultivos. En el ciclo invernal 2009 se sembró trigo y en el ciclo estival 2009 – 2010 soja y maíz. Los cultivos respondieron a la aplicación de potasio y el grado de respuesta estuvo inversamente relacionado con la concentración de K+ intercambiable en la camada del suelo de 0 – 10 cm. El nivel crítico fue 0,17 cmolc kg-1. En promedio, la dosis de máxima producción fue 63 kg K2O ha-1. Al agruparse los datos de esta investigación con los de otras investigaciones recientes se estableció el nivel crítico general para potasio en 0,16 cmolc kg-1. Los resultados contribuyeron para mejorar la recomendación de fertilización potásica en la Región Oriental de Paragua

Recent advances in methodology for clinical trials in small populations : the InSPiRe project

Where there are a limited number of patients, such as in a rare disease, clinical trials in these small populations present several challenges, including statistical issues. This led to an EU FP7 call for proposals in 2013. One of the three projects funded was the Innovative Methodology for Small Populations Research (InSPiRe) project. This paper summarizes the main results of the project, which was completed in 2017. The InSPiRe project has led to development of novel statistical methodology for clinical trials in small populations in four areas. We have explored new decision-making methods for small population clinical trials using a Bayesian decision-theoretic framework to compare costs with potential benefits, developed approaches for targeted treatment trials, enabling simultaneous identification of subgroups and confirmation of treatment effect for these patients, worked on early phase clinical trial design and on extrapolation from adult to pediatric studies, developing methods to enable use of pharmacokinetics and pharmacodynamics data, and also developed improved robust meta-analysis methods for a small number of trials to support the planning, analysis and interpretation of a trial as well as enabling extrapolation between patient groups. In addition to scientific publications, we have contributed to regulatory guidance and produced free software in order to facilitate implementation of the novel methods