Random effects model

About: Random effects model is a research topic. Over the lifetime, 8388 publications have been published within this topic receiving 438823 citations. The topic is also known as: random effects & random effect.

Comparative performance of heterogeneity variance estimators in meta-analysis: a review of simulation studies.

Abstract: Random-effects meta-analysis methods include an estimate of between-study heterogeneity variance. We present a systematic review of simulation studies comparing the performance of different estimation methods for this parameter. We summarise the performance of methods in relation to estimation of heterogeneity and of the overall effect estimate, and of confidence intervals for the latter. Among the twelve included simulation studies, the DerSimonian and Laird method was most commonly evaluated. This estimate is negatively biased when heterogeneity is moderate to high and therefore most studies recommended alternatives. The Paule-Mandel method was recommended by three studies: it is simple to implement, is less biased than DerSimonian and Laird and performs well in meta-analyses with dichotomous and continuous outcomes. In many of the included simulation studies, results were based on data that do not represent meta-analyses observed in practice, and only small selections of methods were compared. Furthermore, potential conflicts of interest were present when authors of novel methods interpreted their results. On the basis of current evidence, we provisionally recommend the Paule-Mandel method for estimating the heterogeneity variance, and using this estimate to calculate the mean effect and its 95% confidence interval. However, further simulation studies are required to draw firm conclusions. Copyright © 2016 John Wiley & Sons, Ltd.

Efficient Conjoint Choice Designs in the Presence of Respondent Heterogeneity

Abstract: Random effects or mixed logit models are often used to model differences in consumer preferences. Data from choice experiments are needed to estimate the mean vector and the variances of the multivariate heterogeneity distribution involved. In this paper, an efficient algorithm is proposed to construct semi-Bayesian D-optimal mixed logit designs that take into account the uncertainty about the mean vector of the distribution. These designs are compared to locally D-optimal mixed logit designs, Bayesian and locally D-optimal designs for the multinomial logit model and to nearly orthogonal designs Sawtooth CBC for a wide range of parameter values. It is found that the semi-Bayesian mixed logit designs outperform the competing designs not only in terms of estimation efficiency but also in terms of prediction accuracy. In particular, it is shown that assuming large prior values for the variance parameters for constructing semi-Bayesian mixed logit designs is most robust against the misspecification of the prior mean vector. In addition, the semi-Bayesian mixed logit designs are compared to the fully Bayesian mixed logit designs, which take also into account the uncertainty about the variances in the heterogeneity distribution and which can be constructed only using prohibitively large computing power. The differences in estimation and prediction accuracy turn out to be rather small in most cases, which indicates that the semi-Bayesian approach is currently the most appropriate one if one needs to estimate mixed logit models.

On false discovery control under dependence

Abstract: A popular framework for false discovery control is the random effects model in which the null hypotheses are assumed to be independent. This paper generalizes the random effects model to a conditional dependence model which allows dependence between null hypotheses. The dependence can be useful to characterize the spatial structure of the null hypotheses. Asymptotic properties of false discovery proportions and numbers of rejected hypotheses are explored and a large-sample distributional theory is obtained.

Meta-analysis of standardized incidence and mortality rates of childhood leukaemia in proximity to nuclear facilities

Abstract: The meta-analysis combined and statistically analysed studies of childhood leukaemia and nuclear facilities. Focus was on studies that calculated standardized rates for individual facilities. Due to variability between study designs, eight separate analyses were performed stratified by age and zone. One hundred and thirty-six sites were used in at least one analysis. Unadjusted, fixed effects and random effects models were used. Meta-rates greater than one were found in all models at all stratification levels often achieving statistical significance. Caution must be used when interpreting these results. The meta-analysis was able to show an increase in childhood leukaemia near nuclear facilities, but does not support a hypothesis to explain the excess. Each type of model utilized has limitations. Fixed effects models give greater weight to larger studies; however, population density may be a risk factor. Random effects models give greater weight to smaller studies that may be more likely to be affected by publication bias. A limitation of the overall study design is that standardized rates must be available for individual sites which led to exclusion of studies that only calculated rates for multiple sites and those that presented other statistical methods. Further, dose-response studies do not support excess rates found near nuclear facilities. However, it cannot be ignored that the majority of studies have found elevated rates, although not usually statistically significant.