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A latent autoregressive model for longitudinal binary data subject to informative missingness.

Paul S Albert1, Dean A Follmann, Shaohua A Wang

  • 1Biometric Research Branch, National Cancer Institute, National Institutes of Health, Bethesda, Maryland 20892, USA. albertp@ctep.nci.nih.gov

Biometrics
|September 17, 2002
PubMed
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This study introduces a new statistical model for analyzing longitudinal binary data, crucial for clinical trials with missing patient information. The model improves accuracy by accounting for patient data patterns over time.

Area of Science:

  • Biostatistics
  • Clinical Trials Methodology
  • Longitudinal Data Analysis

Background:

  • Longitudinal clinical trials frequently generate extensive binary outcome data.
  • Opiate addiction treatment studies often involve repeated binary urine tests to monitor substance use.
  • Missing data, due to patient dropout or intermittent non-responses, poses a significant challenge in analyzing such data.

Purpose of the Study:

  • To develop and present a novel statistical model for longitudinal binary data that accounts for informative missingness.
  • To extend existing models by incorporating autocorrelation within a latent autoregressive framework.
  • To compare the marginal probability of positive urine tests over time between treatment groups in an opiate addiction trial.

Main Methods:

Related Experiment Videos

  • A latent autoregressive model was developed, linking the binary response and missing-data processes via a shared Gaussian autoregressive process.
  • This shared process induces informative missingness, capturing dependencies over time.
  • Parameter estimation was performed using the Monte Carlo Expectation-Maximization (EM) algorithm.

    • Main Results:

    • Simulations demonstrated that including within-subject autocorrelation is vital for accurate analysis of longitudinal binary data with informative missingness.
    • The proposed latent autoregressive model effectively handles autocorrelation and informative missingness.
    • Application to opiate addiction trial data illustrated the methodology's practical utility.

    Conclusions:

    • The novel latent autoregressive model provides a robust framework for analyzing longitudinal binary data with informative missingness.
    • Accounting for within-subject autocorrelation is essential for unbiased estimation of treatment effects in such settings.
    • This methodology enhances the analysis of clinical trial data, particularly in substance abuse research.