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Open Access Research

Improving clinical trial efficiency by biomarker-guided patient selection

Ruud Boessen1*, Hiddo J Lambers Heerspink2, Dick De Zeeuw2, Diederick E Grobbee1, Rolf HH Groenwold1 and Kit CB Roes1

Author Affiliations

1 Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Universiteitsweg 100, 3584 CG, Utrecht, The Netherlands

2 Department of Clinical Pharmacology, University Medical Center Groningen, Antonius Deusinglaan 1, 9713 AV, Groningen, The Netherlands

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Trials 2014, 15:103  doi:10.1186/1745-6215-15-103

Published: 2 April 2014

Abstract

Background

In many therapeutic areas, individual patient markers have been identified that are associated with differential treatment response. These markers include both baseline characteristics, as well as short-term changes following treatment. Using such predictive markers to select subjects for inclusion in randomized clinical trials could potentially result in more targeted studies and reduce the number of subjects to recruit.

Methods

This study compared three trial designs on the sample size needed to establish treatment efficacy across a range of realistic scenarios. A conventional parallel group design served as the point of reference, while the alternative designs selected subjects on either a baseline characteristic or an early improvement after a short active run-in phase. Data were generated using a model that characterized the effect of treatment on survival as a combination of a primary effect, an interaction with a baseline marker and/or an early marker improvement. A representative scenario derived from empirical data was also evaluated.

Results

Simulations showed that an active run-in design could substantially reduce the number of subjects to recruit when improvement during active run-in was a reliable predictor of differential treatment response. In this case, the baseline selection design was also more efficient than the parallel group design, but less efficient than the active run-in design with an equally restricted population. For most scenarios, however, the advantage of the baseline selection design was limited.

Conclusions

An active run-in design could substantially reduce the number of subjects to recruit in a randomized clinical trial. However, just as with the baseline selection design, generalizability of results may be limited and implementation could be difficult.

Keywords:
Clinical trial designs; Biomarkers; Baseline selection; Active run-in