Impact of missing data on type 1 error rates in non-inferiority trials

In this paper, a simulation study is conducted to systematically investigate the impact of different types of missing data on six different statistical analyses: four different likelihood-based linear mixed effects models and analysis of covariance (ANCOVA) using two different data sets, in non-inferiority trial settings for the analysis of longitudinal continuous data. ANCOVA is valid when the missing data are completely at random. Likelihood-based linear mixed effects model approaches are valid when the missing data are at random. Pattern-mixture model (PMM) was developed to incorporate non-random missing mechanism. Our simulations suggest that two linear mixed effects models using unstructured covariance matrix for within-subject correlation with no random effects or first-order autoregressive covariance matrix for within-subject correlation with random coefficient effects provide well control of type 1 error (T1E) rate when the missing data are completely at random or at random. ANCOVA using last observation carried forward imputed data set is the worst method in terms of bias and T1E rate. PMM does not show much improvement on controlling T1E rate compared with other linear mixed effects models when the missing data are not at random but is markedly inferior when the missing data are at random.