We propose a framework for the use of sensitivity analyses in the regulatory setting. Sensitivity analyses should not consist of a few methods that might have been reasonable alternatives to the chosen primary analysis method, nor they should they explore only a local or limited space of violations in the assumptions of the primary analysis. Instead, sensitivity analyses should systematically and comprehensively explore the space of possible assumptions to evaluate whether the key conclusions hold up under all plausible scenarios. For example, in the presence of missing data, tipping point analyses can be used to vary assumptions about missing outcomes on the experimental and control arms in order to identify and discuss the plausibility of scenarios under which there is no longer evidence of a treatment effect. We introduce a simple, novel tipping point approach in which inference on the treatment effect is based on the observed data and the sensitivity parameters, with minimal assumptions and no need for imputation. The sensitivity parameters to be varied are the mean differences between outcomes in dropouts and outcomes in completers on each of the two treatment arms. We derive the asymptotic properties of the proposed statistic and compare the approach to alternative tipping point methods introduced in the literature.