This book presents, at a non-technical level, several approaches for the analysis of correlated data: mixed models for continuous and categorical outcomes, nonparametric methods for repeated measures and growth mixture models for heterogeneous trajectories over time.
This book presents, at a non-technical level, several approaches for the analysis of correlated data: mixed models for continuous and categorical outcomes, nonparametric methods for repeated measures and growth mixture models for heterogeneous trajectories over time.
Ralitza Gueorguieva is a Senior Research Scientist at the Department of Biostatistics, Yale School of Public Health. She has more than 20 years experience in statistical methodology development and collaborations with psychiatrists and other researchers, and is the author of over 130 peer-reviewed publications.
Inhaltsangabe
Introduction Traditional Methods for Analysis of Longitudinal and Clustered Data Linear Mixed Models for Longitudinal and Clustered Data Linear Models for Non-normal Outcomes Nonparametric Methods for the Analysis of Repeatedly Measured Data Post-hoc Analysis and Adjustments for Multiple Comparisons Handling of Missing Data and Dropout in Longitudinal Studies Controlling for Covariates in Studies with Repeated Measures Assessment of Moderator and Mediator Effects Mixture Models for Trajectory Analyses Study Design and Sample Size Calculations Summary and Further Readings
Introduction Traditional Methods for Analysis of Longitudinal and Clustered Data Linear Mixed Models for Longitudinal and Clustered Data Linear Models for Non-normal Outcomes Nonparametric Methods for the Analysis of Repeatedly Measured Data Post-hoc Analysis and Adjustments for Multiple Comparisons Handling of Missing Data and Dropout in Longitudinal Studies Controlling for Covariates in Studies with Repeated Measures Assessment of Moderator and Mediator Effects Mixture Models for Trajectory Analyses Study Design and Sample Size Calculations Summary and Further Readings
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