We review recent developments in the design and analysis of group-randomized trials (GRTs). Regarding design, we summarize developments in estimates of intraclass correlation, power analysis, matched designs, designs involving one group per condition, and designs in which individuals are randomized to receive treatments in groups. Regarding analysis, we summarize developments in marginal and conditional models, the sandwich estimator, model-based estimators, binary data, survival analysis, randomization tests, survey methods, latent variable methods and nonlinear mixed models, time series methods, global tests for multiple endpoints, mediation effects, missing data, trial reporting, and software. We encourage investigators who conduct GRTs to become familiar with these developments and to collaborate with methodologists who can strengthen the design and analysis of their trials.

Design and analysis of group-randomized trials: a review of recent methodological developments.

In psychology and the behavioral sciences generally, the use of the hierarchical linear model (HLM) and its extensions for discrete outcomes are popular methods for modeling clustered data. HLM and its discrete outcome extensions, however, are certainly not the only methods available to model clustered data. Although other methods exist and are widely implemented in other disciplines, it seems that psychologists have yet to consider these methods in substantive studies. This article compares and contrasts HLM with alternative methods including generalized estimating equations and cluster-robust standard errors. These alternative methods do not model random effects and thus make a smaller number of assumptions and are interpreted identically to single-level methods with the benefit that estimates are adjusted to reflect clustering of observations. Situations where these alternative methods may be advantageous are discussed including research questions where random effects are and are not required, when random effects can change the interpretation of regression coefficients, challenges of modeling with random effects with discrete outcomes, and examples of published psychology articles that use HLM that may have benefitted from using alternative methods. Illustrative examples are provided and discussed to demonstrate the advantages of the alternative methods and also when HLM would be the preferred method. (PsycINFO Database Record

On the unnecessary ubiquity of hierarchical linear modeling.

This tutorial presents an overview of multilevel or hierarchical data modelling and its applications in medicine. A description of the basic model for nested data is given and it is shown how this can be extended to fit flexible models for repeated measures data and more complex structures involving cross-classifications and multiple membership patterns within the software package MLwiN. A variety of response types are covered and both frequentist and Bayesian estimation methods are described.

/pdf/multilevel-modelling-of-medical-data-1i4011bbwb.pdf

Multilevel modelling of medical data

Preferred reporting items for overviews of systematic reviews including harms checklist: a pilot tool to be used for balanced reporting of benefits and harms.

This review provides an introduction to the role of statistical modelling of complex social situations, in particular considering the application of advanced methodologies to an educational context By building an overview of techniques such as cross classified multi-level modelling and multiple membership structures and through a comprehensive review of the application of these techniques the review should inform advances in educational research The application of these advanced statistical techniques which allow for hierarchically structured data will assist the Department in accurately identifying the individual and independent effects of various factors on attainment and in particular the simultaneous effects of area of residence effects and school effects in educational production functions

/pdf/cross-classified-and-multiple-membership-structures-in-4eh5dgvsz8.pdf

Cross-classified and multiple membership structures in multilevel models : an introduction and review

We review five software packages that can fit a generalized linear mixed model for data with more than a two-level structure and a multiple number of independent variables. These five packages are MLn, MLwiN, SAS Proc Mixed (Glimmix Macro), HLM, and VARCL. We first discuss the features of each of the five packages. These features include data input and data management, statistical model capabilities, output, and user friendliness. We then compare their performance on several simulated datasets.

Statistical Computing Software Reviews Comparisons of Software Packages for Generalized Linear Multilevel Models

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Statistical Computing Software Reviews Comparisons of Software Packages for Generalized Linear Multilevel Models