Search

There has been substantial research interest in developing various estimation procedures for varying coefficient models. Most methods in the literature require specifying a working covariance struc...

Trade and investment between developing regions such as China and Latin America (LATAM) are growing prominently. However, insights on crucial factors such as innovation in business and management (iBM) about both regions have not been scrutinized. This study presents the research output, impact, and structure of iBM research published about China and LATAM in a comparative framework using Google Scholar, Dimensions, and Microsoft Academic. Findings showed i) that iBM topics of both regions were framed within research and development management, and technological development topics, ii) significant differences in output and impact between regions, and iii) the same case for platforms.

With the advancement of modern technologies, intensive longitudinal studies, where subjects get intensively measured in a relatively short period of time, have become popular in psychological and biomedical area. Challenges arise in better analyzing the rich amount of data and at the same time avoiding biased statistical inference introduced by potential informative missingness. Building upon previous studies by Lin et al. and Kapura et al., we aim to address the problem of informative missingness in the context of multivariate intensive longitudinal outcomes. In this article, we present a multivariate shared-parameter model, where the multivariate intensive longitudinal outcomes are modeled by a mixed-effects locations scale model and further linked with the corresponding missing mechanism by sharing the subject random effects. The proposed model is then estimated using Bayesian sampling approach. An adolescent mood study example is illustrated and results show that joint modeling of the mood assessments not only provide more accurate effect estimates, but also valuable information on the associations between multiple outcomes.

Repeated measurements are widely encountered in medical or pharmaceutical studies, which can be analyzed by both longitudinal data and functional data analysis methods, particularly when the underlying process is continuous and the number of measurement points is not too small. Motivated by real problems of clustering patient profiles in clinical trials, this paper gives an overview of the clustering methods for repeated measurement data and compares three longitudinal data methods and two functional data methods with extensive simulation studies. Methods with appropriate properties are applied to the real data to produce interpretable results.

KAF156 belongs to a new class of antimalarial agents (imidazolopiperazines), with activity against asexual and sexual blood stages and the preerythrocytic liver stages of malarial parasites.We conducted a phase 2, open-label, two-part study at five centers in Thailand and Vietnam to assess the antimalarial efficacy, safety, and pharmacokinetic profile of KAF156 in adults with acute Plasmodium vivax or P. falciparum malaria. Assessment of parasite clearance rates in cohorts of patients with vivax or falciparum malaria who were treated with multiple doses (400 mg once daily for 3 days) was followed by assessment of the cure rate at 28 days in a separate cohort of patients with falciparum malaria who received a single dose (800 mg).Median parasite clearance times were 45 hours (interquartile range, 42 to 48) in 10 patients with falciparum malaria and 24 hours (interquartile range, 20 to 30) in 10 patients with vivax malaria after treatment with the multiple-dose regimen and 49 hours (interquartile range, 42 to 54) in 21 patients with falciparum malaria after treatment with the single dose. Among the 21 patients who received the single dose and were followed for 28 days, 1 had reinfection and 7 had recrudescent infections (cure rate, 67%; 95% credible interval, 46 to 84). The mean (±SD) KAF156 terminal elimination half-life was 44.1±8.9 hours. There were no serious adverse events in this small study. The most common adverse events included sinus bradycardia, thrombocytopenia, hypokalemia, anemia, and hyperbilirubinemia. Vomiting of grade 2 or higher occurred in 2 patients, 1 of whom discontinued treatment because of repeated vomiting after receiving the single 800-mg dose. More adverse events were reported in the single-dose cohort, which had longer follow-up, than in the multiple-dose cohorts.KAF156 showed antimalarial activity without evident safety concerns in a small number of adults with uncomplicated P. vivax or P. falciparum malaria. (Funded by Novartis and others; ClinicalTrials.gov number, NCT01753323 .).

Partial differential equation (PDE) models are commonly used to model complex dynamic systems in applied sciences such as biology and finance. The forms of these PDE models are usually proposed by experts based on their prior knowledge and understanding of the dynamic system. Parameters in PDE models often have interesting scientific interpretations, but their values are often unknown and need to be estimated from the measurements of the dynamic system in the presence of measurement errors. Most PDEs used in practice have no analytic solutions, and can only be solved with numerical methods. Currently, methods for estimating PDE parameters require repeatedly solving PDEs numerically under thousands of candidate parameter values, and thus the computational load is high. In this article, we propose two methods to estimate parameters in PDE models: a parameter cascading method and a Bayesian approach. In both methods, the underlying dynamic process modeled with the PDE model is represented via basis function expansion. For the parameter cascading method, we develop two nested levels of optimization to estimate the PDE parameters. For the Bayesian method, we develop a joint model for data and the PDE and develop a novel hierarchical model allowing us to employ Markov chain Monte Carlo (MCMC) techniques to make posterior inference. Simulation studies show that the Bayesian method and parameter cascading method are comparable, and both outperform other available methods in terms of estimation accuracy. The two methods are demonstrated by estimating parameters in a PDE model from long-range infrared light detection and ranging data. Supplementary materials for this article are available online. © 2013 American Statistical Association.

The article addresses the problem of detecting presence and location of a small low emission source inside of an object, when the background noise dominates. This problem arises, for instance, in some homeland security applications. The goal is to reach the signal-to-noise ratio (SNR) levels on the order of $10^{-3}$. A Bayesian approach to this problem is implemented in 2D. The method allows inference not only about the existence of the source, but also about its location. We derive Bayes factors for model selection and estimation of location based on Markov Chain Monte Carlo (MCMC) simulation. A simulation study shows that with sufficiently high total emission level, our method can effectively locate the source.