title: Improving clinical trial efficiency by biomarker-guided patient selection.
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.
authors.affiliation: Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Universiteitsweg 100, 3584 CG, Utrecht, The Netherlands. ruud.boessen@TNO.nl.
authors.forename: Ruud
authors.initials: R
authors.lastname: Boessen
authors.forename: Hiddo J Lambers
authors.initials: HJ
authors.lastname: Heerspink
authors.forename: Dick
authors.initials: D
authors.lastname: De Zeeuw
authors.forename: Diederick E
authors.initials: DE
authors.lastname: Grobbee
authors.forename: Rolf H H
authors.initials: RH
authors.lastname: Groenwold
authors.forename: Kit C B
authors.initials: KC
authors.lastname: Roes
mesh_terms: D000959:Antihypertensive Agents / Q000627:therapeutic use; D015415:Biomarkers / Q000032:analysis*; D002986:Clinical Trials as Topic / Q000379:methods*; D003198:Computer Simulation; D003920:Diabetes Mellitus / Q000175:diagnosis / Q000188:drug therapy / Q000378:metabolism; D006801:Humans; D015233:Models, Statistical; D018579:Patient Selection; D011237:Predictive Value of Tests; D018401:Sample Size; D016896:Treatment Outcome
chemical_list: D000959:Antihypertensive Agents; D015415:Biomarkers