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Modelling and simulation of experimental designs to find the best design of randomized clinical trials in a rare disease: Cystic fibrosis

Polina Kurbatova, Agathe Bajard, Harm Tiddens, Vitaly Volpert, Catherine Cornu, Nicolai Bessonov, Behrouz Kassai, Sylvie Chabaud, Patrice Nony, Daan Caudri
European Respiratory Journal 2014 44: P1220; DOI:
Polina Kurbatova
1Centre d'Investigation Clinique Lyon, Université Claude Bernard, Lyon, France
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Agathe Bajard
2Unité de Biostatistique et d'Evaluation des Thérapeutiques, Centre Léon Bérard, Lyon, France
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Harm Tiddens
3Department Respiratory Medicine, Erasmus MC-Sophia Children's Hospital, Rotterdam, Netherlands
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Vitaly Volpert
4Institut Camille Jordan, Université Claude Bernard, Lyon, France
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Catherine Cornu
5Centre d'Investigation Clinique Lyon, Hopital Louis Pradel, Bron, France
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Nicolai Bessonov
5Centre d'Investigation Clinique Lyon, Hopital Louis Pradel, Bron, France
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Behrouz Kassai
1Centre d'Investigation Clinique Lyon, Université Claude Bernard, Lyon, France
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Sylvie Chabaud
2Unité de Biostatistique et d'Evaluation des Thérapeutiques, Centre Léon Bérard, Lyon, France
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Patrice Nony
1Centre d'Investigation Clinique Lyon, Université Claude Bernard, Lyon, France
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Daan Caudri
3Department Respiratory Medicine, Erasmus MC-Sophia Children's Hospital, Rotterdam, Netherlands
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Abstract

Introduction: The parallel group randomized controlled trial is the gold standard in clinical trials, but in rare diseases alternative trial designs may be more suitable. An in silico approach with modelling and simulation has been proposed in the CRESim project to find the most appropriate design in cystic fibrosis (CF).

Methods: A mathematical model was built to describe the effect of Dornase alfa on mucocillary clearance in CF. Secondly, a virtual patient population was created and the randomization of these patients in clinical trials was simulated. Several experimental designs were simulated: Parallel, Cross-over, Randomized withdrawal, Early escape, N of 1, and Adaptative randomizations such as “Play the winner” (PW) and “Drop the loser” (DL). The results of simulations of these different experimental designs were compared with the traditional parallel group design, in terms of precision of the estimation of treatment effect, statistical power, and trial duration.

Results: One thousand trials were simulated for each design with a sample size of 50 patients. The Cross-over design showed the best estimation of treatment effect with 87% statistical power, a low coefficient of variation (36%) but with a high trial duration (2 years). Parallel, PW and DL designs all had a similar power (about 60%) and coefficient of variation (about 45%) but PW and DL had a higher trial duration (2 years vs. 1.5 years). The N of 1 design had the lowest power (22%), which could be improved by a meta-analysis of N of 1 trials.

Conclusion: This in silico approach could be used to find the most effective clinical trial design, especially in rare diseases such as CF.

  • Cystic fibrosis
  • Experimental approaches
  • Epidemiology
  • © 2014 ERS
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Modelling and simulation of experimental designs to find the best design of randomized clinical trials in a rare disease: Cystic fibrosis
Polina Kurbatova, Agathe Bajard, Harm Tiddens, Vitaly Volpert, Catherine Cornu, Nicolai Bessonov, Behrouz Kassai, Sylvie Chabaud, Patrice Nony, Daan Caudri
European Respiratory Journal Sep 2014, 44 (Suppl 58) P1220;

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Modelling and simulation of experimental designs to find the best design of randomized clinical trials in a rare disease: Cystic fibrosis
Polina Kurbatova, Agathe Bajard, Harm Tiddens, Vitaly Volpert, Catherine Cornu, Nicolai Bessonov, Behrouz Kassai, Sylvie Chabaud, Patrice Nony, Daan Caudri
European Respiratory Journal Sep 2014, 44 (Suppl 58) P1220;
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