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Assessing change with longitudinal and clustered binary data.

J M Neuhaus1

  • 1Department of Epidemiology and Biostatistics, University of California, San Francisco, California 94143-0560, USA. john@biostat.ucsf.edu

Annual Review of Public Health
|March 29, 2001
PubMed
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This study addresses challenges in analyzing longitudinal and clustered binary data, focusing on within-subject changes. It presents methods for accurately estimating covariate effects in repeated measures, crucial for understanding subject response over time.

Area of Science:

  • Biostatistics
  • Longitudinal Data Analysis
  • Clinical Trials

Background:

  • Repeated measures are common in studies to track subject response changes.
  • Existing methods often focus on between-subject effects, not within-subject changes.
  • Analyzing clustered binary data presents unique statistical challenges.

Purpose of the Study:

  • To address statistical issues in assessing within-subject changes in longitudinal and clustered binary data.
  • To compare popular methods with cluster-specific models for analyzing repeated binary outcomes.
  • To propose and describe approaches for consistent estimation of within-subject covariate effects.

Main Methods:

  • Decomposition of covariates into between- and within-cluster components.
  • Utilizing cluster-specific models to assess within-subject effects.

Related Experiment Videos

  • Describing statistical approaches yielding consistent estimates.

    • Main Results:

    • Many standard methods primarily capture cross-sectional or between-subject effects.
    • Cluster-specific models can assess within-subject effects but introduce complications.
    • Proposed methods provide consistent estimates of within-subject covariate effects.

    Conclusions:

    • Accurate assessment of within-subject changes is vital for longitudinal studies.
    • Decomposing covariates is key to disentangling between- and within-subject effects.
    • The presented approaches offer reliable methods for analyzing complex repeated binary data.