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Longitudinal data analysis (repeated measures) in clinical trials.

P S Albert1

  • 1Biometric Research Branch, National Cancer Institute, CTEP, DCTDC Executive Plaza North, 6130 Executive Blvd, MSC 7434 Bethesda, MD 20892-7434, USA.

Statistics in Medicine
|July 17, 1999
PubMed
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This review explores methods for analyzing longitudinal data in clinical trials, focusing on Gaussian and discrete outcomes. It provides practical examples and discusses crucial trial aspects like missing data and sequential monitoring.

Area of Science:

  • Biostatistics
  • Clinical Trials Methodology
  • Longitudinal Data Analysis

Background:

  • Longitudinal data is frequently collected in clinical trials to track disease progression over time.
  • Analyzing this data is crucial for understanding treatment effects and disease dynamics.

Purpose of the Study:

  • To review and summarize methodological research on longitudinal data analysis specifically for clinical trials.
  • To provide practical guidance and examples for applying these methods to clinical trial data.

Main Methods:

  • Discussion of methodologies for analyzing both Gaussian and discrete longitudinal data.
  • Illustration of methods using five real-world clinical trial examples with longitudinal outcomes.
  • Exploration of key clinical trial considerations such as sequential monitoring and missing data adjustments.

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Main Results:

  • Comprehensive overview of statistical techniques applicable to longitudinal clinical trial data.
  • Demonstration of how various analytical methods can be effectively applied.
  • Identification of important practical issues and their potential solutions.

Conclusions:

  • The paper offers a valuable resource for researchers and statisticians involved in clinical trials with longitudinal data.
  • It highlights the importance of appropriate analytical methods for robust clinical trial interpretation.
  • The review includes a survey of available software for longitudinal data analysis.