A major problem in the analysis of clinical trials is missing data from patients who drop out of the study before the predetermined schedule. In this paper we consider the situation where the outcome measure is a continuous variable and the final outcome at the end of the study is the main interest. We argue that the hypothetical complete-data marginal mean averaged over the dropout patterns is not as relevant clinically as the conditional mean of the completers together with the probability of completion or dropping out of the trial. We first take the pattern-mixture modelling approach to factoring the likelihood function, then direct the analysis to the multiple testings of a composite of hypotheses that involves the probability of dropouts and the conditional mean of the completers. We review three types of closed step-down multiple-testing procedures for this application. Data from several clinical trials are used to illustrate the proposed approach.