Handling of Discontinuation of Treatment in non-ignorable Situations

Clinical studies mostly generate incomplete data. The fraction of non-available data can range from small to substantial, and the reasons can be manifold: The data was not recorded, the data was lost on its way to the clinical database, or patients discontinued treatment and were lost to follow-up. In all those cases there is no problem in analyzing the complete data only if the missingness is completely random. However, if partial or missing data is dependent on other variables, that process must be modeled in order to correct for the bias that would otherwise result. In such cases, complete case analyses (“per protocol”) are inadequate, and so are imputation methods tha