title: An in silico approach helped to identify the best experimental design, population, and outcome for future randomized clinical trials.
abstract: OBJECTIVE
  The main objective of our work was to compare different randomized clinical trial (RCT) experimental designs in terms of power, accuracy of the estimation of treatment effect, and number of patients receiving active treatment using in silico simulations.
  
  
  METHODS
  A virtual population of patients was simulated and randomized in potential clinical trials. Treatment effect was modeled using a dose-effect relation for quantitative or qualitative outcomes. Different experimental designs were considered, and performances between designs were compared. One thousand clinical trials were simulated for each design based on an example of modeled disease.
  
  
  RESULTS
  According to simulation results, the number of patients needed to reach 80% power was 50 for crossover, 60 for parallel or randomized withdrawal, 65 for drop the loser (DL), and 70 for early escape or play the winner (PW). For a given sample size, each design had its own advantage: low duration (parallel, early escape), high statistical power and precision (crossover), and higher number of patients receiving the active treatment (PW and DL).
  
  
  CONCLUSIONS
  Our approach can help to identify the best experimental design, population, and outcome for future RCTs. This may be particularly useful for drug development in rare diseases, theragnostic approaches, or personalized medicine.
authors.affiliation: Centre de Lutte Contre le Cancer Léon Bérard, Unité de Biostatistique et d'Evaluation des Thérapeutiques, 28, rue Laënnec, Lyon 69373, France.
authors.forename: Agathe
authors.initials: A
authors.lastname: Bajard
authors.affiliation: Centre de Lutte Contre le Cancer Léon Bérard, Unité de Biostatistique et d'Evaluation des Thérapeutiques, 28, rue Laënnec, Lyon 69373, France. Electronic address: sylvie.chabaud@lyon.unicancer.fr.
authors.forename: Sylvie
authors.initials: S
authors.lastname: Chabaud
authors.affiliation: CHU Lyon, Service de Pharmacologie Clinique, Lyon, France; Université Lyon 1, UMR 5558 CNRS, Lyon, France; Hôpital Louis Pradel, Centre d'Investigation Clinique et Essais Thérapeutiques, INSERM CIC1407, Bron, France.
authors.forename: Catherine
authors.initials: C
authors.lastname: Cornu
authors.affiliation: Hôpital Louis Pradel, Centre d'Investigation Clinique et Essais Thérapeutiques, INSERM CIC1407, Bron, France.
authors.forename: Anne-Charlotte
authors.initials: AC
authors.lastname: Castellan
authors.affiliation: CHU Lyon, Service de Pharmacologie Clinique, Lyon, France; Université Lyon 1, UMR 5558 CNRS, Lyon, France; Hôpital Louis Pradel, Centre d'Investigation Clinique et Essais Thérapeutiques, INSERM CIC1407, Bron, France.
authors.forename: Salma
authors.initials: S
authors.lastname: Malik
authors.affiliation: Université Lyon 1, UMR 5558 CNRS, Lyon, France; Institut Camille Jordan UMR 5208, Université Claude Bernard Lyon 1, France.
authors.forename: Polina
authors.initials: P
authors.lastname: Kurbatova
authors.affiliation: Institut Camille Jordan UMR 5208, Université Claude Bernard Lyon 1, France.
authors.forename: Vitaly
authors.initials: V
authors.lastname: Volpert
authors.affiliation: Institut Camille Jordan UMR 5208, Université Claude Bernard Lyon 1, France.
authors.forename: Nathalie
authors.initials: N
authors.lastname: Eymard
authors.affiliation: CHU Lyon, Service de Pharmacologie Clinique, Lyon, France; Université Lyon 1, UMR 5558 CNRS, Lyon, France; Hôpital Louis Pradel, Centre d'Investigation Clinique et Essais Thérapeutiques, INSERM CIC1407, Bron, France.
authors.forename: Behrouz
authors.initials: B
authors.lastname: Kassai
authors.affiliation: CHU Lyon, Service de Pharmacologie Clinique, Lyon, France; Université Lyon 1, UMR 5558 CNRS, Lyon, France.
authors.forename: Patrice
authors.initials: P
authors.lastname: Nony
mesh_terms: D003198:Computer Simulation; D018592:Cross-Over Studies; D005544:Forecasting; D006801:Humans; D008881:Migraine Disorders / Q000188:drug therapy; D016032:Randomized Controlled Trials as Topic / Q000592:standards*; D012107:Research Design; D018170:Sumatriptan / Q000627:therapeutic use
chemical_list: D018170:Sumatriptan
keywords: Experimental design; Modeling; Randomized controlled clinical trial; Simulation; Statistical analysis; Therapeutic evaluation