mixedpower: A new program for calculating power and sample size for longitudinal mixed models

Power and sample-size calculations for clinical trials with longitudinal continuous outcome measures are typically performed using simulation, with a perception that analytic solutions are either complex or intractable. However, there is a straightforward general approach using matrix algebra that is perhaps not widely appreciated and has been under-utilized. A new community-contributed command, mixedpower, instantaneously performs these calculations for two-level mixed models, allowing for a wide variety of treatment-effect specifications and covariance structures, including “marginal” models where the within-subject error terms are correlated across time. The command can also estimate the impact on power and resulting bias from incorrectly assuming a specific treatment effect, such as a proportionate slope change. Other essential requirements of a realistic trial, such as dropout, staggered recruitment, and unequal allocation ratios are also readily incorporated. A key feature is the ability to enter variance parameters either manually or automatically, extracted from a fitted model in memory, saving both time and potential mistakes. Other companion programs are introduced such as mvmixedpower for multivariate mixed models when there is more than one outcome, and trial counts, which helps the user easily specify plausible levels of data missingness due to partial follow-up and dropout.