title: Generating Virtual Patients by Multivariate and Discrete Re-Sampling Techniques.
abstract: OBJECTIVE
  Clinical Trial Simulations (CTS) are a valuable tool for decision-making during drug development. However, to obtain realistic simulation scenarios, the patients included in the CTS must be representative of the target population. This is particularly important when covariate effects exist that may affect the outcome of a trial. The objective of our investigation was to evaluate and compare CTS results using re-sampling from a population pool and multivariate distributions to simulate patient covariates.
  
  
  METHODS
  COPD was selected as paradigm disease for the purposes of our analysis, FEV1 was used as response measure and the effects of a hypothetical intervention were evaluated in different populations in order to assess the predictive performance of the two methods.
  
  
  RESULTS
  Our results show that the multivariate distribution method produces realistic covariate correlations, comparable to the real population. Moreover, it allows simulation of patient characteristics beyond the limits of inclusion and exclusion criteria in historical protocols.
  
  
  CONCLUSIONS
  Both methods, discrete resampling and multivariate distribution generate realistic pools of virtual patients. However the use of a multivariate distribution enable more flexible simulation scenarios since it is not necessarily bound to the existing covariate combinations in the available clinical data sets.
authors.affiliation: Division of Pharmacology, Leiden Academic Centre for Drug Research, Leiden, The Netherlands.
authors.forename: D
authors.initials: D
authors.lastname: Teutonico
authors.affiliation: Division of Pharmacology, Leiden Academic Centre for Drug Research, Leiden, The Netherlands.
authors.forename: F
authors.initials: F
authors.lastname: Musuamba
authors.affiliation: Clinical Pharmacology Modelling & Simulation, GlaxoSmithKline, Stockley Park, Middlesex, UK.
authors.forename: H J
authors.initials: HJ
authors.lastname: Maas
authors.affiliation: Department of Pharmacometrics, Nycomed GmbH, Constance, Germany.
authors.forename: A
authors.initials: A
authors.lastname: Facius
authors.affiliation: Clinical Pharmacology Modelling & Simulation, GlaxoSmithKline, Stockley Park, Middlesex, UK.
authors.forename: S
authors.initials: S
authors.lastname: Yang
authors.affiliation: Division of Pharmacology, Leiden Academic Centre for Drug Research, Leiden, The Netherlands.
authors.forename: M
authors.initials: M
authors.lastname: Danhof
authors.affiliation: Division of Pharmacology, Leiden Academic Centre for Drug Research, Leiden, The Netherlands. o.dellapasqua@ucl.ac.uk.|Clinical Pharmacology Modelling & Simulation, GlaxoSmithKline, Stockley Park, Middlesex, UK. o.dellapasqua@ucl.ac.uk.|Clinical Pharmacology & Therapeutics, University College London, BMA House, Tavistock Square, London, WC1H 9JP, UK. o.dellapasqua@ucl.ac.uk.
authors.forename: O
authors.initials: O
authors.lastname: Della Pasqua
mesh_terms: D000328:Adult; D000368:Aged; D000369:Aged, 80 and over; D002986:Clinical Trials as Topic; D003198:Computer Simulation; D003657:Decision Making; D005260:Female; D006801:Humans; D008297:Male; D008875:Middle Aged; D029424:Pulmonary Disease, Chronic Obstructive / Q000188:drug therapy
keywords: clinical trial simulations; covariate effect; demographics; drug development; inclusion and exclusion criteria; multivariate distribution; re-sampling