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Drop-outs and a random regression model

J E Overall1

  • 1Department of Psychiatry and Behavioral Sciences, University of Texas School at Houston, USA.

Journal of Biopharmaceutical Statistics
|July 1, 1997
PubMed
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Drop-outs significantly reduce the power of random regression model (RRM) tests for treatment differences. Simple endpoint analyses with baseline and time-in-treatment covaried offer a more robust alternative against power loss in clinical trials.

Area of Science:

  • Biostatistics
  • Clinical Trial Design
  • Statistical Power Analysis

Background:

  • Randomized parallel-groups designs are common in clinical trials.
  • Assessing treatment differences in the rate of change is crucial.
  • The impact of missing data due to drop-outs on statistical tests needs investigation.

Purpose of the Study:

  • To investigate the implications of drop-outs on the power of random regression model (RRM) tests.
  • To compare the robustness of RRM tests versus simple endpoint analyses in the presence of drop-outs.

Main Methods:

  • Monte Carlo simulation methods were employed.
  • A two-stage random regression model (RRM) was fitted using least squares linear regression.
  • Significance tests (ANOVA/ANCOVA) were applied to slope coefficients.

Related Experiment Videos

  • Simple endpoint analyses with baseline and time-in-treatment covaried were also evaluated.

    • Main Results:

    • RRM tests showed adequate protection against Type I error.
    • The power of RRM tests was significantly reduced by the presence of drop-outs.
    • Simple endpoint analyses demonstrated greater robustness against power degradation caused by drop-outs.

    Conclusions:

    • Drop-outs pose a substantial threat to the statistical power of RRM tests in parallel-group studies.
    • Simple endpoint analyses adjusting for baseline and time-in-treatment are more resilient to power loss from drop-outs.
    • Researchers should consider alternative analytical methods when drop-outs are anticipated.