Power estimation in biomarker studies where events are already observed

Background: 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 specific conditions to satisfy for such evaluations to be more than hypothesis generating. One condition they proposed is that archived tissues must be available on a sufficiently large number of patients from the pivotal trials to ensure adequately powered analyses. Purpose: The purpose of this article is to provide a new perspective on how to estimate power for assessing the prognostic and predictive values of a single binary biomarker in prospective–retrospective biomarker studies. Computer programs are provided to facilitate the use of these methods in practice. Methods: The proposed methods utilize 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. Conclusion: Simon et al. proposed a new paradigm for biomarker design, conduct, analysis, and evaluation in prospective–retrospective studies that offers a more efficient alternative than fully prospective biomarker studies. As a general rule, they suggest that samples from at least two-thirds of the patients be available for analysis. In this article, I expand on this issue and provide a methodological tool useful for estimating study power in prospective–retrospective biomarker studies. It is my hope that these incremental efforts to improve the quality and statistical rigor in biomarker studies will hasten the introduction of useful tumor biomarkers into clinical practice.