Novel methodology is implemented to assess the predictive power of covariate information associated with sequential binary events. Logistic models are first fitted on the basis of a subset of the observations and then evaluated sequentially on the rest. The probabilistic forecasts are compared to the outcomes via a scoring function, but as most validation samples are small, the usual reference distribution for the test statistics is inadequate. However, bootstrap-based distributions can easily be constructed. The first example pertains to the evaluation of screening tests for major depression. It illustrates that goodness-of-fit and predictive assessments lead to the selection of very different models. The second example deals with the prediction of a major event in the natural history of HIV-induced disease. It shows that this type of analysis can reveal features missed by other approaches.