Power for Multilevel Analysis

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<h2>Power for Multilevel Analysis</h2> <ol> <li> <p><a href="http://www.bristol.ac.uk/cmm/learning/multilevel-models/samples.html" rel="nofollow">Centre for Multilevel Modelling: Sample sizes for multilevel models</a> using <code>MLPowSim</code></p> </li> <li> <p><a href="http://rpsychologist.com/introducing-powerlmm" rel="nofollow">Introducing <code>powerlmm</code> an R package for power calculations for longitudinal multilevel models</a> </p> <ul> <li>The purpose of <code>powerlmm</code> is to help design <strong>longitudinal treatment studies</strong>, with or without higher-level clustering (e.g. by therapists, groups, or physician), and missing data. </li> <li>Currently, <code>powerlmm</code> supports two-level models, nested three-level models, and partially nested models. </li> <li>Additionally, unbalanced designs and missing data can be accounted for in the calculations. </li> <li>Power is calculated analytically, but simulation methods are also provided in order to evaluated bias, type 1 error, and the consequences of model misspecification. </li> <li>For novice R users, the basic functionality is also provided as a Shiny web application.</li> </ul> </li> <li> <p><a href="https://cran.r-project.org/web/packages/longpower/vignettes/longpower.pdf" rel="nofollow">Power for linear models of longitudinal data with applications to Alzheimer’s Disease Phase II study design</a> using <code>longpower</code></p> </li> <li>We will discuss power and sample size estimation for randomized placebo controlled studies in which the primary inference is based on the <strong>interaction of treatment and time</strong> in a <strong>linear mixed effects model</strong> (Laird and Ware, 1982). <ul> <li>We will demonstrate how the sample size formulas of (Liu and Liang, 1997) for <strong>marginal</strong> or model fit by generalized estimating equation (GEE) (Zeger and Liang, 1986) can be adapted for mixed effects models. </li> </ul> </li> <li> <p>Finally, using mixed effects model estimates based on data from the Alzheimer’s Disease Neuroimaging Initiative (ADNI), we will give <strong>examples of sample size calculations</strong> for models with and without baseline covariates which may help explain <strong>heterogeneity</strong> in cognitive decline and improve power.</p> </li> <li> <p><a href="http://xz6kg9rb2j.search.serialssolutions.com/?url_ver=Z39.88-2004&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.jtitle=Methods%20in%20Ecology%20and%20Evolution&rft.stitle=Methods%20Ecol%20Evol&rft.atitle=SIMR:%20an%20R%20package%20for%20power%20analysis%20of%20generalized%20linear%20mixed%20models%20by%20simulation&rft.volume=7&rft.issue=4&rft.spage=493&rft.epage=498&rft.date=2016-04-01&rft.aulast=Green&rft.aufirst=Peter&rft.issn=2041-210X&rft.eissn=2041-210X&rfr_id=info:sid/wiley.com:OnlineLibrary" rel="nofollow"><code>simR</code> an R package for power analysis of generalized linear mixed models by simulation</a></p> <ul> <li>The R package <code>simR</code> allows users to calculate power for <strong>generalized linear mixed models</strong> from the <code>lme4</code> package. The power calculations are based on Monte Carlo simulations.</li> <li>It includes to 'ols' for <ul> <li>running a power analysis for a give n model and design</li> <li>calculating powe rcurves to assess trade-offs between power and sample size</li> </ul> </li> <li>This paper presents a tutorial using a simple example of count data with mixed effects (with structure represen-tative of environmental monitoring data) to guide the user along a gentle learning curve, adding only a few com-mands or options at a time.</li> </ul> </li> </ol>
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