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This study introduces a new approach for interim analyses in clinical trials using Mixed Effects Models for Repeated Measures (MMRM). The AC-Hybrid-approach controls overall alpha by applying MMRM to all data and using complete-case fractions for stopping boundaries.

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Area of Science:

  • Biostatistics
  • Clinical Trial Methodology
  • Longitudinal Data Analysis

Background:

  • Mixed Effects Models for Repeated Measures (MMRM) is standard for longitudinal clinical trial data.
  • Implementing interim analyses with MMRM while controlling overall alpha remains challenging.
  • Standard group sequential testing (GST) methods assume statistical independence, which is violated by within-subject correlations in MMRM.

Purpose of the Study:

  • To propose and validate a novel approach for interim analyses in clinical trials using MMRM.
  • To ensure robust control of the overall Type I error rate (alpha) under the MMRM framework.
  • To provide a method that addresses regulatory concerns regarding alpha control with estimated correlations.

Main Methods:

  • The proposed AC-Hybrid-approach applies the MMRM framework to all available data at each interim analysis timepoint.
  • Group sequential stopping boundaries are derived using complete-case information fractions.
  • The method's theoretical properties are proven to control overall alpha irrespective of within-subject measurement correlations.

Main Results:

  • The AC-Hybrid-approach demonstrates effective control of the overall alpha in clinical trials employing MMRM.
  • The study provides examples illustrating the impact of this approach on alpha and statistical power.
  • Theoretical proofs confirm alpha control regardless of the degree of within-subject correlation.

Conclusions:

  • The AC-Hybrid-approach offers a statistically sound and potentially regulatorily acceptable method for interim analyses in MMRM-based clinical trials.
  • This method enhances the reliability of clinical trial results by properly managing Type I error.
  • Further exploration of the approach's impact on power is recommended.