Principles of multilevel modelling.

By Sander Greenland. International Journal of Epidemiology, 2000

498 citations recorded by the source corpus.

Subfields: Genetics, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/37e3fcf91f9db8011b4a3848f15e629249e47335

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Citations in the corpus

The (mis)estimation of neighborhood effects: causal inference for a practicable social epidemiology. (2004, background): “…to as multilevel, hierarchical linear, random coefficient, random effect, mixed, empirical Bayes, Laird-Ware, or variance component models.11 DiezRoux (2000, 2002), Greenland (2000), Sullivan et al. (1999), and Murray (1998) offer accessible introductions of such models for epidemiologists.”
CONSORT 2010 Explanation and Elaboration: updated guidelines for reporting parallel group randomised trials (2010, methodology): “Data analysis should be based on counting each participant once [178,179] or should be done by using more complex statistical procedures [180].”
CONSORT 2010 explanation and elaboration: updated guidelines for reporting parallel group randomised trials. (2012, methodology): “Data analysis should be based on counting each participant once [178,179] or should be done by using more complex statistical procedures [180].”
Power and Predictive Accuracy of Polygenic Risk Scores (2013, background): “Shrinkage estimation leads to reduced mean square error for prediction and has some other advantages [35,36], but in the main applications for polygenic scores to date, namely association testing and AUC, it does not improve over the linear regression estimate.”
The Revised CONSORT Statement for Reporting Randomized Trials: Explanation and Elaboration (2001, methodology): “Data analysis should be based on counting each participant once (128, 129) or should be done by using more complex statistical procedures (130).”
Statistical Applications in Genetics and Molecular Biology A Shrinkage Approach to Large-Scale Covariance Matrix Estimation and Implications for Functional Genomics (2011, unknown): “In this case the quantity E(T) is interpreted as prior mean and λ as a hyper-parameter that may be estimated from the data by optimizing the marginal likelihood (e.g., Morris, 1983; Greenland, 2000).”