Controlling alpha for mixed effects models for repeated measures.

Mixed Effects Models for Repeated Measures (MMRM) is often used in clinical trials with longitudinal data. However, there has not been an in-depth examination available on how investigators can implement interim analysis while also controlling the overall alpha for clinical trials under an MMRM analysis framework. Statistical independence among measurements, which is often assumed in group sequential testing (GST), is not valid under an MMRM framework due to the correlations induced by longitudinal within-subject measurements. Therefore, methods associated with GST derived under independence need to be adjusted accordingly. While these correlations can be estimated from the study data, regulatory agencies may not accept results based on these estimated correlations since there is no guarantee that the overall alpha is strongly controlled. In this article, we propose a new AC-Hybrid-approach for controlling the overall alpha. The AC-Hybrid-approach has two key attributes. First, we apply the MMRM analysis framework on all available data at every analysis timepoint. Second, we use complete-case information fractions to derive the group sequential stopping boundaries. We prove that the overall alpha is controlled regardless of the correlations among within-subject measurements. We also show the impact of this approach on the alpha and the power through examples.