Maastricht University

About: Maastricht University is a education organization based out in Maastricht, Limburg, Netherlands. It is known for research contribution in the topics: Population & Health care. The organization has 19263 authors who have published 53291 publications receiving 2266866 citations. The organization is also known as: Universiteit Maastricht & UM.

European Society of Hypertension guidelines for blood pressure monitoring at home: a summary report of the Second International Consensus Conference on Home Blood Pressure Monitoring.

Abstract: This document summarizes the available evidence and provides recommendations on the use of home blood pressure monitoring in clinical practice and in research. It updates the previous recommendations on the same topic issued in year 2000. The main topics addressed include the methodology of home blood pressure monitoring, its diagnostic and therapeutic thresholds, its clinical applications in hypertension, with specific reference to special populations, and its applications in research. The final section deals with the problems related to the implementation of these recommendations in clinical practice.

Bias and Efficiency of Meta-Analytic Variance Estimators in the Random-Effects Model:

Abstract: The meta-analytic random effects model assumes that the variability in effect size estimates drawn from a set of studies can be decomposed into two parts: heterogeneity due to random population effects and sampling variance. In this context, the usual goal is to estimate the central tendency and the amount of heterogeneity in the population effect sizes. The amount of heterogeneity in a set of effect sizes has implications regarding the interpretation of the meta-analytic findings and often serves as an indicator for the presence of potential moderator variables. Five population heterogeneity estimators were compared in this article analytically and via Monte Carlo simulations with respect to their bias and efficiency.

Methods to estimate the between-study variance and its uncertainty in meta-analysis

Abstract: Meta-analyses are typically used to estimate the overall/mean of an outcome of interest. However, inference about between-study variability, which is typically modelled using a between-study variance parameter, is usually an additional aim. The DerSimonian and Laird method, currently widely used by default to estimate the between-study variance, has been long challenged. Our aim is to identify known methods for estimation of the between-study variance and its corresponding uncertainty, and to summarise the simulation and empirical evidence that compares them. We identified 16 estimators for the between-study variance, seven methods to calculate confidence intervals, and several comparative studies. Simulation studies suggest that for both dichotomous and continuous data the estimator proposed by Paule and Mandel and for continuous data the restricted maximum likelihood estimator are better alternatives to estimate the between-study variance. Based on the scenarios and results presented in the published studies, we recommend the Q-profile method and the alternative approach based on a 'generalised Cochran between-study variance statistic' to compute corresponding confidence intervals around the resulting estimates. Our recommendations are based on a qualitative evaluation of the existing literature and expert consensus. Evidence-based recommendations require an extensive simulation study where all methods would be compared under the same scenarios.

International Evidence on Ethical Mutual Fund Performance and Investment Style

Abstract: Using an international database containing 103 German, UK and US ethical mutual funds we review and extend previous research on ethical mutual fund performance. By applying a Carhart multi-factor model [Carhart, Journal of Finance 57 (1997) 57] we overcome the benchmark problem most prior ethical studies suffered from. After controlling for investment style, we find no evidence of significant differences in risk-adjusted returns between ethical and conventional funds for the 1990–2001 period. Our results also suggest that ethical mutual funds underwent a catching up phase, before delivering financial returns similar to those of conventional mutual funds. Finally, our performance estimates are robust to the inclusion of ethical indexes, which, surprisingly, are not incrementally capable of explaining ethical mutual fund return variation.