Tipping point analysis (TPA) is gaining popularity in regulatory submissions in recent years. This presentation will review why TPA gets popular in clinical trial submissions, its relationship with missing data mechanisms/assumptions and other imputation methods to address missingness. The implementation methods of TPA for binary endpoint and for continuous endpoint will be examined separately, different visual presentations of TPA results by using SAS and R are compared, their interpretations and choice are discussed. For TPA for continuous variables, the method of Frequentist approach is compared with Bayesian approach for imputation/simulation, based on which TPA is conducted. The presentation will also list some real examples of TPA application in FDA submissions. The significance of TPA as a sensitivity analysis to support primary analysis will be summarized through the demonstration of real examples.