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Power Analysis of Trials with Multilevel Data is a valuable reference for anyone who wants to perform power calculations on trials with hierarchical data. It provides a thorough overview of power analysis, familiarizing you with terminology and notation, outlining the key concepts of statistical power and power analysis, and covering all common hierarchical designs. Content: List of figures List of tables Preface Introduction Experimentation Hierarchical data structures Research design Power analysis for experimental research Aim and contents of the book Multilevel statistical models The basic two-level model Estimation and hypothesis test Intraclass correlation coefficient Multilevel models for dichotomous outcomes More than two levels of nesting Software for multilevel analysis Concepts of statistical power analysis Background of power analysis Types of power analysis Timing of power analysis Methods for power analysis Robustness of power and sample size calculations Procedure for a priori power analysis The optimal design of experiments Sample size and precision analysis Sample size and accuracy of parameter estimates Cluster randomized trials Introduction Multilevel model Sample size calculations for continuous outcomes Sample size calculations for dichotomous outcomes An example Improving statistical power in cluster randomized trials Inclusion of covariates Minimization, matching, pre-stratification Taking repeated measurements Crossover in cluster randomized trials Stepped wedge designs Multisite trials Introduction Multilevel model Sample size calculations for continuous outcomes Sample size calculations for dichotomous outcomes An example Pseudo cluster randomized trials Introduction Multilevel model Sample size calculations for continuous outcomes Sample size calculations for binary outcomes An example Individually randomized group treatment trials Introduction Multilevel model Sample size calculations for continuous outcomes Sample size calculations for dichotomous outcomes An example Longitudinal intervention studies Introduction Multilevel model Sample size calculations for continuous outcomes Sample size calculations for dichotomous outcomes The effect of drop-out on statistical power An example Extensions: three levels of nesting and factorial designs Introduction Three-level cluster randomized trials Multisite cluster randomized trials Repeated measures in cluster randomized trials and multisite trials Factorial designs The problem of unknown intraclass correlation coefficients Estimates from previous research Sample size re-estimation Bayesian sample size calculation Maximin optimal designs Computer software for power calculations Introduction Computer program SPA-ML References Author Index Subject Index Power Analysis of Trials with Multilevel Data covers using power and sample size calculations to design trials that involve nested data structures. The book gives a thorough overview of power analysis that details terminology and notation, outlines key concepts of statistical power and power analysis, and explains why they are necessary in trial design. It guides you in performing power calculations with hierarchical data, which enables more effective trial design. The authors are leading experts in the field who recognize that power analysis has attracted attention from applied statisticians i ..