random-effects

Note that a random effects model does not 'take account' of the heterogeneity, in the sense that it is no longer an issue.

Second-order fractional polynomials and random effects meta-regression models were used for modeling the dose-risk relation.

When there is heterogeneity that cannot readily be explained, one analytical approach is to incorporate it into a random effects model.

When the answers do not coincide, the random effects estimate may not reflect the actual effect in any particular population being studied.

Random-effects models are widely used to characterize repeated measurement data.

Results Repeated measurement obtained reasonable results by the fixed and random effects along with efficient estimate of covariance matrix.

Variance component model of the random effects of covariance matrix unit for the "Linear model introduction" matrix has been studied.

The random effects estimate and its confidence interval address the question 'what is the average treatment effect?

It is difficult to establish the validity of any distributional assumption, and this is a common criticism of random effects meta-analyses.

Q function have been used to study the statistical diagnostics for the exponential family nonlinear models with random effects.

The Six Sigma Black Belt should know the difference between the various types of experimental models (fixed-effects, random-effects, mixed).

In this paper, the random effects of covariance matrix positive for the promotion of research.

Two reviewers extracted data, which were aggregated using random-effects models.

The summary relative risks (RRs) were estimated by the random effects model.

Pooled relative risk (RR) and 95% confidence interval (CI) were calculated using a random-effects model.

Summary relative risks (RRs) were estimated using a random effects model.

Objective To discuss random effects Poisson regression model forrepeated count data in clinical trial.

Pooled effect sizes were calculated using random effects models.

Pooled relative risks (RRs) were estimated by random effects models.

Estimates were pooled using a random-effects model.

Using the primary data, the study-specific relative risks (RRs) were calculated and then pooled using a random effects model.

With the development of regression theory, the random effects model has become an important research topic.

Statistical Diagnosis of Nonlinear Models with Random Effects in Terms of Statistical Curvature

Empirical Bayes Estimation of Variance Components in Two-Way Classification Random Effects Model

The least square estimate of covariance matrices in the growth curve model with random effects

Asymptotically Optimal Empirical Bayes Estimation for Variance Components in Random Effects Models

Asymptotic Properties in Nonlinear Reproductive Dispersion Models with Random Effects

Influences of Different Additional Random Effects on Estimating Covariance Functions

The parameter estimation and properties on exponential family nonlinear model with random effects

General Admissibility of Linear Predictor in the Multivariate Random Effects Model

Some Asymptotic Inferences about Nonlinear Model with Random Effects

assessing occupational exposure via the two - way random effects model

repeated measurements will show variation because of these random effects

Local Influence Assessment in a One-way Random Effects Model

LM test for first-order serial correlation, assuming no random effects

On Confidence Region of Nonlinear Models with Random Effects

LM Test for random effects, assuming no serial correlation

LM Joint test for random effects and serial correlation

Test of random effects with incomplete panel data