Missing Data and Non-compliance Data in Clinical End-point Equivalence Studies
Stella Grosser, FDA  Carol Kim, FDA  *Wanjie Sun, FDA  Aotian Yang, GWU 

Keywords: Missing data, Non-compliance data, Bioequivalence, Generics

In clinical trials, patients may drop out for various reasons (e.g. lack of efficacy, treatment-related side-effects, or factors unrelated to the trial) (Heyting et al 1992). Non-compliance also happens very often, such as poor compliance rate, out-of-window visits, and so on. Drop out and non-compliance can be balanced or imbalanced between treatment groups. Methods to handle missing data have been studied extensively in superiority trials for new drugs (Little and Rubin 2002, Molenberghs and Kenward 2007, Ibramhim JG and Molenberghs 2009, Siddiqui et al 2009, etc.). However, the impact of missing data and non-compliance data on bioequivalence trials for generic drugs, particularly, clinical end-point equivalence studies, has seldom been investigated. With the institution of the Generic Drug User Fee Act (GDUFA) in 2012, it becomes a pressing task to study the dropout pattern in the current ANDA submissions, to test the robustness of the current practice for handling missing and non-compliance data in clinical end-point equivalence studies, and to propose feasible sensitivity methods. In this presentation, a meta-analysis will be discussed to evaluate the missing data/non-compliance patterns in the ANDA clinical end-point bioequivalence studies using topical drugs for treatment of acne vulgaris as an example. Simulation results will be presented to evaluate the bias and efficiency of the current practice under different missing data mechanisms (missing completely at random, missing at random, missing not at random) and different non-compliance mechanisms. Sensitivity analysis will be briefly discussed in bioequivalence tests.