In immunology clinical trials, primary endpoint usually is a binary composite endpoint assessed at the end of treatment, with some components collected longitudinally, while others collected only at the end of treatment. Missing data is common in such trials and often follows monotone missing. Non-responder Imputation (NRI), which treats all missing data as non-responder, is commonly adopted. It is controversial that NRI might be useful for handling missing data due to safety consideration. Though NRI is considered conservative with respect to response rate, we show MH Chi-square test based on NRI can be invalid for testing treatment effect under missing at random assumption. Since primary endpoint is defined at the end of treatment, we do not necessarily wish to evaluate the joint distribution among longitudinal outcomes. Therefore, we propose two tests, one based on inverse propensity weighting (IPW) with all observed longitudinal outcomes involved in the propensity, the other based on multivariate imputation by chained equation (MICE). Extensive simulations are carried out to present that type I errors are well controlled for the proposed methods, whereas inflated for NRI.