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sin ho j cluster randomization trials statistical design analysis 2024

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Textbook in PDF format Oftentimes, small groups (called clusters) of individuals (called subunits) are randomized between treatment arms. Typically, clusters are families, classes, communities, surgeons operating patients, and so on. Such trials are called cluster randomization trials (CRTs). The subunits in each cluster share common frailties so that their outcomes tend to be positively correlated. Since clusters are independent, the data in two arms are independent in CRTs. In a clinical trial, multiple sites (such as teeth or ears) from each subject may be randomized between different treatment arms. In this case, the sites (subunits) of each subject (cluster) share common genetic, physiological, or environmental characteristics so that their observations tend to be positively correlated. This kind of trials are called subunit randomization trials (SRTs). In SRTs, dependency exists both within and between treatment arms. Individually randomized group treatment (IRGT) trials are composite of traditional independent subject randomization and CRTs. In an IRGT trial, the control arm is to treat patients individually, whereas the experimental arm is to treat patients using a group training, education, or treatment to increase the treatment effect by close interactions among patients. As a result, the outcome data of the control arm are independent as in traditional trials, but those in the experimental arm are correlated within each group (cluster) as in CRTs. Hence, two arms in IRGT trials have different dependency structures. Unlike standard CRTs, clusters of IRGT trials are usually organized after randomization. But statistically, they have identical statistical issues between the two types of trials, i.e., accounting for the dependency within each cluster. Although this book is entitled Cluster Randomization Trials, it covers all three types of trials (i.e., CRTs, SRTs, and IRGT trials) resulting in clustered data. For outcome variables of binary, continuous, and time-to-event Preface Introduction References One-Sample Binary Data Estimation of Binomial Proportions Modified McNemar's Test for Clustered Paired Binary Data Clustered McNemar’s Test Real Data Example Sample Size Calculations for Clustered Binary Data Example NumericalStudies References Chi-Square Test for Two-Sample Clustered Binary Data (I): Donner's Adjustment Donner's Test Statistic Validation of Donner's Test Distribution of Donner’s Test Statistic Numerical Studies Example References Chi-Square Test for Two-Sample Clustered Binary Data (II): GEE Adjustment Modified Chi-Square Test with GEE-Type Adjustment Test Statistic based on Optimal Estimators Efficiency of Weighted Estimators Sample Size Calculation Numerical Studies Example Simulations References Subunit Randomization Trials: GEE-Type Test for Two-Sample Clustered Binary Data Modified Chi-Square Test with GEE-Type Adjustment Sample size Calculation Numerical Studies Simulations Real Data Example References Random Number Generation of Clustered Binary Data Lunn-Davies Method Kang-Jung Method Basic Algorithm Beta-Binomial Distribution Method based on Multivariate Normal Random Variables Under Cluster Randomization Under Subunit Randomization References Tests for RC Contingency Tables with Clustered Categorical Data Tests for 2K Contingency Tables with Clustered Ordered Categorical Data Score Tests for Independent Data Score Tests for Clustered Data Simulation Studies Real Data Examples Chi-Square Test for RC Contingency Tables with Clustered Categorical Data Simulation Studies Real Data Analysis References Clustered Continuous Data Cluster Randomization Trials Modified t-Test for CRTs Sample Size Calculation for CRTs Subunit Randomization Trials Modified t-Test for SRTs Sample Size Calculation for SRTs An Equivalence Test for Clustered Pair Data Equivalence Test Example Inference of Medians for Paired Survival Data Statistical Testing on Paired Median Survival Times Sample Size for Testing on Paired Median Survival Times Confidence Interval of Ratio or Difference of Median Survival Times Real Data Examples Simulation Studies References Rank Tests for Matched Survival Data and Sample Size Calculation Weighted Rank Tests for Matched Survival Data Paired Two-Sample Data Case Matched K-Sample Data Case Real Data Examples Simulation Studies Sample Size Calculation for the Weighted Rank Tests with Paired Survival Data Sample Size under Some Practical Settings When Accrual Rate Is Given Instead of Accrual Period When Historical Data Are Available with Both of the Paired Subjects Treated by the Same Treatment Simulation Studies Examples Discussion References Rank Tests for Clustered Survival Data and Sample Size Calculation under Cluster Randomization Weighted Rank Tests for Clustered Survival Data Extension To K Sample Cases Simulation Studies Real Example Sample Size Calculation of the Log-Rank Test for Two-Arm CRTs Specification of Survival Distribution Specification of Censoring Distribution Sample Size Calculation under Practical Design Settings Sample Size Calculation for given Accrual Rate instead of Accrual Period Simulation Studies Examples References Rank Tests for Clustered Survival Data and Sample Size Calculation under Subunit Randomization Two-Sample Weighted Rank Tests for Subunit Randomization Trials Shared Gamma Frailty Model Simulation Studies Examples Sample Size Calculation of the Log-Rank Test for SRTs Specification of Survival Distribution Specification of Censoring Distribution When Accrual Rate Is Specified Instead of Accrual Period Subunit Randomization versus Cluster Randomization Numerical Studies References Group Sequential Testing for Cluster Randomized Trials with Time-to-Event Endpoint Single-Stage Clustered Log-Rank Tests Group Sequential Testing for the Clustered Log-Rank Statistic Estimation of Covariance Matrix and Information Times Maximal Sample Size Numerical Studies Comparison of Stopping Boundaries Simulations Examples Discussion References Random Number Generation of Clustered Survival Data Moran's Method Under Cluster Randomization Sequential Random Number Generation using Conditional Survival Functions Under Cluster Randomization Under Subunit Randomization Gamma-Exponential Frailty Model Under Cluster Randomization Under Subunit Randomization References Cox's Regression for Clustered Survival Data Cox's PHM for Clustered Survival Data Example References Design and Analysis of Individually Randomized Group-Treatment Trials IRGT Trials with Binary Data Modified Chi-Squared Test Sample Size Calculation for the Modified Chi-Squared Test IRGT Trials with Continuous Data Modified t-Test Sample Size Calculation for the Modified t-Test IRGT Trials with Survival Data Rank Test for IRGT Sample Size Calculation for the Log-Rank Test Distributional Assumptions for Sample Size Calculation When Number of Clustersis Given Optimal Designs Numerical Studies Discussion References Analysis of Medical Tests I: Comparison of Concordance Rates with Clustered Data Concordance Rates of Clustered Binary Data Statistical Testing to Compare Concordance Rates Powerand Sample Size Calculation Simulation Studies Example Concordance Rates of Clustered Categorical Data Numerical Studies and Results References Comparison of Binary Medical Tests and ROC Curves with Clustered Data Comparison of Binary Tests with Clustered Data Statistical Testing Methods Simulations Example Comparison of Paired ROC Curves A Statistical Test for Comparing Two Paired ROC Curves Sample Size Calculation Normally Distributed Biomarkers Numerical Studies Comparison of Two ROC Curves with Clustered Pair Data References Index

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sin ho j cluster randomization trials statistical design analysis 2024

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