A confidence interval robust to publication bias for random-effects meta-analysis of few studies.
Sign Up to like & getrecommendations! Published in 2021 at "Research synthesis methods"
DOI: 10.1002/jrsm.1482
Abstract: In meta-analyses including only few studies, the estimation of the between-study heterogeneity is challenging. Furthermore, the assessment of publication bias is difficult as standard methods such as visual inspection or formal hypothesis tests in funnel… read more here.
Keywords: confidence interval; publication; publication bias; random effects ... See more keywords
Bayesian estimation in random effects meta-analysis using a non-informative prior.
Sign Up to like & getrecommendations! Published in 2017 at "Statistics in medicine"
DOI: 10.1002/sim.7156
Abstract: Pooling information from multiple, independent studies (meta-analysis) adds great value to medical research. Random effects models are widely used for this purpose. However, there are many different ways of estimating model parameters, and the choice… read more here.
Keywords: analysis; non informative; random effects; bayesian estimation ... See more keywords
Multilevel model with random effects for clustered survival data with multiple failure outcomes.
Sign Up to like & getrecommendations! Published in 2019 at "Statistics in medicine"
DOI: 10.1002/sim.8041
Abstract: We present a multilevel frailty model for handling serial dependence and simultaneous heterogeneity in survival data with a multilevel structure attributed to clustering of subjects and the presence of multiple failure outcomes. One commonly observes… read more here.
Keywords: multiple failure; methodology; survival data; random effects ... See more keywords
Bayesian network meta-regression hierarchical models using heavy-tailed multivariate random effects with covariate-dependent variances.
Sign Up to like & getrecommendations! Published in 2021 at "Statistics in medicine"
DOI: 10.1002/sim.8983
Abstract: Network meta-analysis (NMA) is gaining popularity in evidence synthesis and network meta-regression allows us to incorporate potentially important covariates into network meta-analysis. In this article, we propose a Bayesian network meta-regression hierarchical model and assume… read more here.
Keywords: random effects; network meta; network; meta regression ... See more keywords
Summarizing empirical information on between‐study heterogeneity for Bayesian random‐effects meta‐analysis
Sign Up to like & getrecommendations! Published in 2022 at "Statistics in Medicine"
DOI: 10.1002/sim.9731
Abstract: In Bayesian meta‐analysis, the specification of prior probabilities for the between‐study heterogeneity is commonly required, and is of particular benefit in situations where only few studies are included. Among the considerations in the set‐up of… read more here.
Keywords: heterogeneity; meta analysis; study heterogeneity; random effects ... See more keywords
The clinical significance of duration of untreated psychosis: an umbrella review and random‐effects meta‐analysis
Sign Up to like & getrecommendations! Published in 2021 at "World Psychiatry"
DOI: 10.1002/wps.20822
Abstract: The idea that a longer duration of untreated psychosis (DUP) leads to poorer outcomes has contributed to extensive changes in mental health services worldwide and has attracted considerable research interest over the past 30 years.… read more here.
Keywords: dup; effects meta; analysis; random effects ... See more keywords
Approximate maximum likelihood estimation for stochastic differential equations with random effects in the drift and the diffusion
Sign Up to like & getrecommendations! Published in 2018 at "Metrika"
DOI: 10.1007/s00184-018-0666-z
Abstract: Consider N independent stochastic processes $$(X_i(t), t\in [0,T])$$(Xi(t),t∈[0,T]), $$i=1,\ldots , N$$i=1,…,N, defined by a stochastic differential equation with random effects where the drift term depends linearly on a random vector $$\Phi _i$$Φi and the diffusion… read more here.
Keywords: effects drift; estimation; diffusion; stochastic differential ... See more keywords
Modeling pedestrian-injury severities in pedestrian-vehicle crashes considering spatiotemporal patterns: Insights from different hierarchical Bayesian random-effects models
Sign Up to like & getrecommendations! Published in 2020 at "Analytic Methods in Accident Research"
DOI: 10.1016/j.amar.2020.100137
Abstract: Abstract To systematically account for the spatiotemporal features and unobserved heterogeneity within pedestrian-vehicle crashes, this paper employs the spatiotemporal analysis and hierarchical Bayesian random-effects models to explore the factors contributing to pedestrian-injury severities of pedestrian-vehicle… read more here.
Keywords: bayesian random; vehicle; random effects; hierarchical bayesian ... See more keywords
A generalized nonlinear mixed-effects height to crown base model for Mongolian oak in northeast China
Sign Up to like & getrecommendations! Published in 2017 at "Forest Ecology and Management"
DOI: 10.1016/j.foreco.2016.09.012
Abstract: Abstract Tree height to crown base (HCB) is an important variable commonly included as one of the predictors in growth and yield models that are the decision-support tools in forest management. In this study, we… read more here.
Keywords: sample; mixed effects; model; random effects ... See more keywords
The importance of being random! Taking full account of random effects in nonlinear sigmoid hierarchical Bayesian models reveals the relationship between deadwood and the species richness of saproxylic beetles
Sign Up to like & getrecommendations! Published in 2020 at "Forest Ecology and Management"
DOI: 10.1016/j.foreco.2020.118064
Abstract: Abstract Hierarchical models are used to study the relationship between a response variable and a predictor in structured data. Random effects are meant to capture the structured part of variability among groups of observations. In… read more here.
Keywords: deadwood; species richness; random effects; relationship ... See more keywords
Correlated random effects models with unbalanced panels
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DOI: 10.1016/j.jeconom.2018.12.010
Abstract: Abstract I propose some strategies for allowing unobserved heterogeneity to be correlated withobserved covariates and sample selection for unbalanced panels. The methods are extensions of the Chamberlain–Mundlak approach for balanced panels when explanatory variables are… read more here.
Keywords: effects models; random effects; correlated random; unbalanced panels ... See more keywords