Comparative randomized inverse sampling

According to previous literature, we define randomized inverse sampling for comparing two treatments with respect to a binary response as the sampling that stops when a total fixed number of successes, irrespective of the treatments, are observed. We have obtained elsewhere the asymptotic distributions for the counting variables involved and have shown them to be equivalent to the corresponding asymptotic distributions for multinomial sampling. In this paper, we start deriving the same basic results using different techniques, and we then show how they give rise to genuinely novel procedures when translated into finite sample approximations. As the main example, a novel confidence interval for the logarithm of the odds ratio of two success probabilities can be constructed in the case of comparative randomized inverse sampling. Some advantages over the standard multinomial sampling in terms of coverage probabilities are visible when no adjustment for cells with zero counts is applied; otherwise, the two sampling schemes appear to be fairly equivalent. This is a reassurance that under certain circumstances, inverse sampling can be safely chosen over more traditional sampling schemes.     

Mixed-effects models for health care longitudinal data with an informative visiting process: A Monte Carlo simulation study

Electronic health records are being increasingly used in medical research to answer more relevant and detailed clinical questions; however, they pose new and significant methodological challenges. For instance, observation times are likely correlated with the underlying disease severity: Patients with worse conditions utilise health care more and may have worse biomarker values recorded. Traditional methods for analysing longitudinal data assume independence between observation times and disease severity; yet, with health care data, such assumptions unlikely hold. Through Monte Carlo simulation, we compare different analytical approaches proposed to account for an informative visiting process to assess whether they lead to unbiased results. Furthermore, we formalise a joint model for the observation process and the longitudinal outcome within an extended joint modelling framework. We illustrate our results using data from a pragmatic trial on enhanced care for individuals with chronic kidney disease, and we introduce user-friendly software that can be used to fit the joint model for the observation process and a longitudinal outcome.     

GROWTH AND INCOME POVERTY IN LATIN AMERICA AND THE CARIBBEAN: EVIDENCE FROM HOUSEHOLD SURVEYS

This paper provides evidence on growth and income poverty in Latin America and the Caribbean (LAC). Results are obtained by processing microdata from household surveys of 18 LAC countries covering the 1990s and early 2000s. Over this period the LAC economies experienced heterogeneous patterns of growth and poverty changes. Most countries in the region had a rather meager performance in terms of poverty reduction. Episodes of positive, significant and unambiguously pro-poor income growth have been rare in Latin America.     

Microeconometric decompositions of aggregate variables: an application to labour informality in Argentina

This article illustrates the use of microeconometric decomposition techniques to characterize changes in aggregate variables. In particular, it studies the effect of changes in the employment structure on the labour informality rate for salaried workers in the greater Buenos Aires area (Argentina). To that aim it computes the difference between the informality rate at moment t and the rate that results from combining the population at moment t with the parameters estimated at moment t that link observable individual characteristics to the informality decision. The article concludes that the deep change of the employment structure in Argentina during the 1980s and the 1990s has had a significant but minor effect on the labour informality rate.