Drop-out often occurs in clinical trials with multiple visits and drop-out is often informative in the sense that the population of patients who dropped out is different from the population of patients who completed the study. To handle data with informative drop-out, an intention-to-treat analysis, which evaluates treatment effects over the population of all randomized patients with at least one post-treatment evaluation, is often required by the regulatory agencies. As a popular and simple intention-to-treat analysis, the last observation carry-forward (LOCF) analysis of variance (ANOVA) performs a statistical test for treatment effects by treating the last observation prior to drop-out as the observation from the last visit. Although discussions, examples and limited empirical results about the LOCF analysis can be found, its theoretical property is unclear. We find that the LOCF one-way ANOVA test is actually asymptotically valid (that is, its asymptotic size is equal to the nominal size) in the special but important case where only two treatments are compared and the two treatment groups have the same number of patients, regardless of whether drop-out is informative or not. In other cases, however, the asymptotic size of the LOCF test is different from the nominal size and is often too small when drop-out is informative, which results in a loss in power of detecting treatment effects, a disadvantage to drug companies. We propose an asymptotically valid test for comparing the global means over subpopulations, where each subpopulation contains patients dropping out after a particular visit. Some simulation results are presented to study the finite sample performance of the LOCF test and our proposed test.