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.

Random-effects models for multivariate repeated measures:

Abstract: Mixed models are widely used for the analysis of one repeatedly measured outcome. If more than one outcome is present, a mixed model can be used for each one. These separate models can be tied together into a multivariate mixed model by specifying a joint distribution for their random effects. This strategy has been used for joining multivariate longitudinal profiles or other types of multivariate repeated data. However, computational problems are likely to occur when the number of outcomes increases. A pairwise modeling approach, in which all possible bivariate mixed models are fitted and where inference follows from pseudo-likelihood arguments, has been proposed to circumvent the dimensional limitations in multivariate mixed models. An analysis on 22-variate longitudinal measurements of hearing thresholds illustrates the performance of the pairwise approach in the context of multivariate linear mixed models. For generalized linear mixed models, a data set containing repeated measurements of seven aspects of psycho-cognitive functioning will be analyzed.

Functional quasi-likelihood regression models with smooth random effects

Abstract: Summary. We propose a class of semiparametric functional regression models to describe the influence of vector-valued covariates on a sample of response curves. Each observed curve is viewed as the realization of a random process, composed of an overall mean function and random components. The finite dimensional covariates influence the random components of the eigenfunction expansion through single-index models that include unknown smooth link and variance functions. The parametric components of the single-index models are estimated via quasi-score estimating equations with link and variance functions being estimated nonparametrically. We obtain several basic asymptotic results. The functional regression models proposed are illustrated with the analysis of a data set consisting of egg laying curves for 1000 female Mediterranean fruit-flies (medflies).

Meta‐analysis for rare events

Abstract: Meta-analysis provides a useful framework for combining information across related studies and has been widely utilized to combine data from clinical studies in order to evaluate treatment efficacy. More recently, meta-analysis has also been used to assess drug safety. However, because adverse events are typically rare, standard methods may not work well in this setting. Most popular methods use fixed or random effects models to combine effect estimates obtained separately for each individual study. In the context of very rare outcomes, effect estimates from individual studies may be unstable or even undefined. We propose alternative approaches based on Poisson random effects models to make inference about the relative risk between two treatment groups. Simulation studies show that the proposed methods perform well when the underlying event rates are low. The methods are illustrated using data from a recent meta-analysis (N. Engl. J. Med. 2007; 356(24):2457-2471) of 48 comparative trials involving rosiglitazone, a type 2 diabetes drug, with respect to its possible cardiovascular toxicity.

Serial correlation in unequally spaced longitudinal data

Abstract: SUMMARY Serial correlation in the within subject error structure in longitudinal data with unequally spaced observations is modelled using continuous time autoregressive moving averages. The models considered have both fixed and random effects in addition to serially correlated within subject errors. Two approaches are presented for calculating the exact likelihood for a model when the errors are Gaussian. The first calculates the covariance matrices for each subject for assumed values of the unknown parameters and estimates the fixed parameters by weighted least squares. The second uses a state space model and the Kalman filter to calculate the exact likelihood. Both methods involve the use of complex arithmetic. Nonlinear optimization is used to obtain maximum likelihood estimates of the parameters.

Richly Parameterized Linear Models: Additive, Time Series, and Spatial Models Using Random Effects

Abstract: Mixed Linear Models: Syntax, Theory, and Methods An Opinionated Survey of Methods for Mixed Linear Models Mixed linear models in the standard formulation Conventional analysis of the mixed linear model Bayesian analysis of the mixed linear model Conventional and Bayesian approaches compared A few words about computing Two More Tools: Alternative Formulation, Measures of Complexity Alternative formulation: The "constraint-case" formulation Measuring the complexity of a mixed linear model fit Richly Parameterized Models as Mixed Linear Models Penalized Splines as Mixed Linear Models Penalized splines: Basis, knots, and penalty More on basis, knots, and penalty Mixed linear model representation Additive Models and Models with Interactions Additive models as mixed linear models Models with interactions Spatial Models as Mixed Linear Models Geostatistical models Models for areal data Two-dimensional penalized splines Time-Series Models as Mixed Linear Models Example: Linear growth model Dynamic linear models in some generality Example of a multi-component DLM Two Other Syntaxes for Richly Parameterized Models Schematic comparison of the syntaxes Gaussian Markov random fields Likelihood inference for models with unobservables From Linear Models to Richly Parameterized Models: Mean Structure Adapting Diagnostics from Linear Models Preliminaries Added variable plots Transforming variables Case influence Residuals Puzzles from Analyzing Real Datasets Four puzzles Overview of the next three chapters A Random Effect Competing with a Fixed Effect Slovenia data: Spatial confounding Kids and crowns: Informative cluster size Differential Shrinkage The simplified model and an overview of the results Details of derivations Conclusion: What might cause differential shrinkage? Competition between Random Effects Collinearity between random effects in three simpler models Testing hypotheses on the optical-imaging data and DLM models Discussion Random Effects Old and New Old-style random effects New-style random effects Practical consequences Conclusion Beyond Linear Models: Variance Structure Mysterious, Inconvenient, or Wrong Results from Real Datasets Periodontal data and the ICAR model Periodontal data and the ICAR with two classes of neighbor pairs Two very different smooths of the same data Misleading zero variance estimates Multiple maxima in posteriors and restricted likelihoods Overview of the remaining chapters Re-Expressing the Restricted Likelihood: Two-Variance Models The re-expression Examples A tentative collection of tools Exploring the Restricted Likelihood for Two-Variance Models Which vj tell us about which variance? Two mysteries explained Extending the Re-Expressed Restricted Likelihood Restricted likelihoods that can and can't be re-expressed Expedients for restricted likelihoods that can't be re-expressed Zero Variance Estimates Some observations about zero variance estimates Some thoughts about tools Multiple Maxima in the Restricted Likelihood and Posterior Restricted likelihoods with multiple local maxima Posteriors with multiple modes