Using a Box–Cox transformation in the analysis of longitudinal data with incomplete responses

We analyse longitudinal data on CD4 cell counts from patients who participated in clinical trials that compared two therapeutic treatments: zidovudine and didanosine. The investigators were interested in modelling the CD4 cell count as a function of treatment, age at base-line and disease stage at base-line. Serious concerns can be raised about the normality assumption of CD4 cell counts that is implicit in many methods and therefore an analysis may have to start with a transformation. Instead of assuming that we know the transformation (e.g. logarithmic) that makes the outcome normal and linearly related to the covariates, we estimate the transformation, by using maximum likelihood, within the Box–Cox family. There has been considerable work on the Box–Cox transformation for univariate regression models. Here, we discuss the Box–Cox transformation for longitudinal regression models when the outcome can be missing over time, and we also implement a maximization method for the likelihood, assumming that the missing data are missing at random.     

Effect of COVID-19 on Household Food Insecurity and Poverty: Evidence from Ghana

Social Indicators Research - Following the outbreak of COVID-19 and its heavy toll on the global community and humanity, a fierce debate on the pandemic and Sustainable Development Goals (SDGs)...     

Reversed preservation of stochastic orders for random minima and maxima with applications

In this paper, reversed preservation properties of right spread order, total time on test order and increasing convex (concave) order when taking random minima and maxima are developed. In this context, reversed preservation properties of some ageing concepts are investigated under parallel (series) systems which are composed of a random number of i.i.d. components. Some applications in reliability and economics are given.     

Bayesian methods for generalized linear models with covariates missing at random

The authors propose methods for Bayesian inference for generalized linear models with missing covariate data. They specify a parametric distribution for the covariates that is written as a sequence of one‐dimensional conditional distributions. They propose an informative class of joint prior distributions for the regression coefficients and the parameters arising from the covariate distributions. They examine the properties of the proposed prior and resulting posterior distributions. They also present a Bayesian criterion for comparing various models, and a calibration is derived for it. A detailed simulation is conducted and two real data sets are examined to demonstrate the methodology.     

Using the EM-algorithm for survival data with incomplete categorical covariates

Incomplete covariate data is a common occurrence in many studies in which the outcome is survival time. With generalized linear models, when the missing covariates are categorical, a useful technique for obtaining parameter estimates is the EM by the method of weights proposed in Ibrahim (1990). In this article, we extend the EM by the method of weights to survival outcomes whose distributions may not fall in the class of generalized linear models. This method requires the estimation of the parameters of the distribution of the covariates. We present a clinical trials example with five covariates, four of which have some missing values.     

War in Iraq: Environment of Insecurity and International Migration

LA GUERRE EN IRAK : CONTEXTE D'INSÉCURITÉ ET MIGRATION INTERNATIONALE
Cet article porte sur les éventuels rapports entre conflit et migration internationale, tout particulièrement dans le contexte de la plus récente guerre en Irak. Les tendances de la migration internationale irakienne sont analysées afin d'établir une éventuelle influence de la guerre sur la migration. Les situations de conflit sont répandues en Irak depuis plus de deux décennies, les racines de ces conflits remontant à 1979, année où Saddam Hussein est devenu le président de ce pays. Pendant le dernier quart de siècle, l'histoire irakienne a été marquée par des guerres et des crises d'aprèsguerre qui ont déclenché des tensions entre ethnies. D'après nous, ce sont ces tensions ethniques qui ont contribuéà la migration internationale à partir de l'Irak. C'est l'instabilité générale associée aux conflits qui engendre souvent des tensions plus vives entre différents groupes ethniques, à savoir les Kurdes, les Turkmènes, les chiites et les sunnites. Les situations de conflit ethnique peuvent mener à un contexte général d'insécurité, lequel sera une « occasion >> pour les personnes qui ont déjà des « projets >> individuels de migration. Les pays d'Europe occidentale seront particulièrement concernés par ces mouvement migratoires potentiels. Pendant les périodes de conflit, les Irakiens ont établi des réseaux de migration et formé en Occident des communautés d'immigrants assez vastes. Ces réseaux serviront également de facteurs facilitants pour la migration ethnique potentielle de certains Irakiens.     

Age-period-cohort analysis: an illustration of the problems in assessing interaction in one observation per cell data

This paper discusses the specific problems of age-period-cohort (A-P-C) analysis within the general framework of interaction assessment for two-way cross-classified data with one observation per cell. The A-P-C multiple classification model containing the effects of age groups (rows), periods of observation (columns), and birth cohorts (diagonals of the two-way table) is characterized as one of a special class of models involving interaction terms assumed to have very specific forms. The so-called A-P-C identification problem, which results from the use of a particular interaction structure for detecting cohort effects, is shown to manifest itself in the form of an exact linear dependency among the columns of the design matrix. The precise relationship holding among these columns is derived, as is an explicit formula for the bias in the parameter estimates resulting from an incorrect specification of an assumed restriction on the parameters required to solve the normal equations. Current methods for modeling A-P-C data are critically reviewed, an illustrative numerical example is presented, and one potentially promising analysis strategy is discussed. However, gien the large number of possible sources for error in A-P-C analyses, it is strongly recommended that the results of such analyses be interpreted with a great deal of caution.     

A Goodness-of-fit Test for Exponentiality Based on the Memoryless Property

In this investigation a test of goodness of fit for exponentiality is proposed. This procedure applies equally whether the scale and/or the location parameters of the distribution are known or not. The limiting null and non-null distributions of the test statistic are normal under minimal conditions. Monte Carlo critical values for small sample sizes are given and the power of the test is calculated for various alternatives showing that it compares favourably relatively to other more complicated published procedures.