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Related Concept Videos

Skewness01:06

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The measures of central tendency calculated from a data set may not reveal much about its intrinsic distribution. If a plot is made of the data set’s values, the mean and the median may not only differ, but also the plot may have more values on one side of the central tendencies. Such a data set is said to be skewed towards that side.
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Basics of Multivariate Analysis in Neuroimaging Data
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A monotone data augmentation algorithm for longitudinal data analysis via multivariate skew-t, skew-normal or t

Yongqiang Tang1

  • 1Department of Biometrics, Tesaro, Waltham, MA, USA.

Statistical Methods in Medical Research
|August 8, 2019
PubMed
Summary

This study introduces a robust statistical model for analyzing skewed longitudinal clinical data. The new method improves handling of non-ignorable dropouts in clinical trials using advanced computational techniques.

Keywords:
Block samplingcontrolled imputationsmixed effects model for repeated measuresmonotone data augmentationpenalized complexity priortipping point analysis

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Area of Science:

  • Biostatistics
  • Longitudinal Data Analysis
  • Clinical Trial Methodology

Background:

  • Mixed effects models for repeated measures are standard for longitudinal clinical data.
  • Existing models may not adequately handle skewed or heavy-tailed data distributions.
  • Non-ignorable dropouts pose challenges in longitudinal clinical trial analysis.

Purpose of the Study:

  • To propose a robust extension of the mixed effects model for repeated measures.
  • To accommodate skewed and heavy-tailed data using a multivariate skew-t distribution.
  • To develop an efficient algorithm for analyzing longitudinal data with non-ignorable dropouts.

Main Methods:

  • Development of a robust mixed effects model based on the multivariate skew-t distribution.
  • Implementation of a Markov chain Monte Carlo algorithm with monotone data augmentation and parameter expansion.
  • Application of controlled pattern imputation for sensitivity analyses in longitudinal clinical trials.

Main Results:

  • The proposed model effectively handles skewed and heavy-tailed longitudinal data.
  • The developed algorithm efficiently performs complex statistical analyses.
  • Sensitivity analyses demonstrate the robustness of the methods in the presence of non-ignorable dropouts.

Conclusions:

  • The robust extension of the mixed effects model provides a valuable tool for analyzing complex longitudinal clinical data.
  • The proposed methodology enhances the reliability of findings from clinical trials with missing data.
  • The study offers practical solutions illustrated with real-world data and SAS programs.