Modelling forces of infection for mumps, rubella and parvovirus: a statistical perspective

The force of infection is a fundamental epidemiological parameter of infectious diseases. For many infectious diseases it is assumed that the force of infection is age-dependant. Although the force of infection can be estimated directly from a follow up study, it is much more common to have cross-sectional seroprevalence data from which the seroprevalence and the force of infection can be estimated. Here we propose to model the seroprevalence with three different parametric models: a nonlinear least squares model proposed by Farrington (1990); a logistic model, estimated using the generalized linear models; two fractional polynomial models of different order. We illustrate the methods on three seroprevalence samples, taken by the literature, regarding the following infectious diseases: mumps, rubella and parvovirus. Besides, in order to determine the optimal sample size for a serological survey, we show the serious problems of the standard confidence interval for a binomial proportion and we introduce some alternative confidence intervals proposed by the literature

Wald type and phi-divergence based test-statistics for isotonic binomial proportions.

In this paper new test statistics are introduced and studied for the important problem of testing hypothesis that involves inequality constraint on proportions when the sample comes from independent binomial random variables: Wald type and phi-divergence based test-statistics. As a particular case of phi-divergence based test-statistics, the classical likelihood ratio test is considered. An illustrative example is given and the performance of all of them for small and moderate sample sizes is analyzed in an extensive simulation study. (C) 2015 International Association for Mathematics and Computers in Simulatio

Untangling hotel industry’s inefficiency: An SFA approach applied to a renowned Portuguese hotel chain

The present paper explores the technical efficiency of four hotels from Teixeira Duarte Group - a renowned Portuguese hotel chain. An efficiency ranking is established from these four hotel units located in Portugal using Stochastic Frontier Analysis. This methodology allows to discriminate between measurement error and systematic inefficiencies in the estimation process enabling to investigate the main inefficiency causes. Several suggestions concerning efficiency improvement are undertaken for each hotel studied.info:eu-repo/semantics/publishedVersio

Timely and reliable evaluation of the effects of interventions: a framework for adaptive meta-analysis (FAME)

Most systematic reviews are retrospective and use aggregate data AD) from publications, meaning they can be unreliable, lag behind therapeutic developments and fail to influence ongoing or new trials. Commonly, the potential influence of unpublished or ongoing trials is overlooked when interpreting results, or determining the value of updating the meta-analysis or need to collect individual participant data (IPD). Therefore, we developed a Framework for Adaptive Metaanalysis (FAME) to determine prospectively the earliest opportunity for reliable AD meta-analysis. We illustrate FAME using two systematic reviews in men with metastatic (M1) and non-metastatic (M0)hormone-sensitive prostate cancer (HSPC)