The clinical utility of a new biomarker should ideally be established in a prospective randomized clinical trial. However, such trials are not always practical. As such, it is common for investigators to identify promising biomarkers using archived specimens and clinical data collected from previously completed therapeutic trials. Simon et al. defined such biomarker studies as prospective–retrospective studies and proposed a new paradigm for biomarker design, conduct, analysis, and evaluation that offers a more efficient alternative than fully prospective biomarker studies. As a general rule, they proposed that archived tissues must be available on a sufficiently large number of patients from the pivotal trials to ensure adequately powered analyses. In this talk, I expand on this issue and provide a methodological tool useful for estimating power for assessing the prognostic and predictive values of a single binary biomarker in prospective–retrospective biomarker studies. Specifically, these methods capitalize on additional information that becomes available during the course of the treatment trial including sample size, accrual time, additional follow-up time, and the observed number of events at time of biomarker analysis. These methods involve solving for the exponential hazard rates that give rise to the event numbers that are consistent with those observed while satisfying other design parameter constraints.