Mixed Models Repeated Measures ANOVA Sample Clauses

Mixed Models Repeated Measures ANOVA. The available-case linear mixed model (LMM) is referred to in clinical trials as the mixed models repeated measures ANOVA (MMRM). It uses all data that has been collected at all time points. This means that the method uses all randomized participants who have missed a few visits or have dropped out of the study which preserves the randomization process and decreases bias. Since there is missingness included in this model, there are often different numbers of participants depending on the time point, and relaxes the assumption that time points must be equally spaced due to the incorporation of random effects. MMRM should be used when there is longitudinal dropout or subject specific effects. A subject specific effect occurs when it is expected that different subjects will exhibit different patterns. A benefit of MMRM is that it can be used when the missing data is classified as missing at random. The results of the pre- post by group test then can be used to test efficacy of treatment. The MMRM model can include both patient-specific intercepts as well as time effects. This model incorporates the subject specific variance into the variance due to error. MMRM’s mathematical representation is shown below. For subject i: yijk = β0 + β0i + β1 ∗ time + b1i ∗ time + β2 ∗ group + β3 ∗ time ∗ group + ejk where j=time points (1, 2, 3,…), k=arm, the β′s are parameter estimates, and ejk~N(0, σ2) MMRM method treats the time variable as a nominal number and is the industry standard. It is a better approach than the repeated measure ANOVA, but it essentially uses the same structure. The benefit of random effects is to allow for individual variation in the fit of intercept and slope rather than force the same pattern to all subjects. Similar comparisons of model estimates are then used to establish efficacy.