Agathe Bajard1, Sylvie Chabaud2, Catherine Cornu3, Anne-Charlotte Castellan4, Salma Malik3, Polina Kurbatova5, Vitaly Volpert6, Nathalie Eymard6, Behrouz Kassai3, Patrice Nony7. 1. Centre de Lutte Contre le Cancer Léon Bérard, Unité de Biostatistique et d'Evaluation des Thérapeutiques, 28, rue Laënnec, Lyon 69373, France. 2. 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. 3. 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. 4. Hôpital Louis Pradel, Centre d'Investigation Clinique et Essais Thérapeutiques, INSERM CIC1407, Bron, France. 5. Université Lyon 1, UMR 5558 CNRS, Lyon, France; Institut Camille Jordan UMR 5208, Université Claude Bernard Lyon 1, France. 6. Institut Camille Jordan UMR 5208, Université Claude Bernard Lyon 1, France. 7. CHU Lyon, Service de Pharmacologie Clinique, Lyon, France; Université Lyon 1, UMR 5558 CNRS, Lyon, France.
Abstract
OBJECTIVES: 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. STUDY DESIGN AND SETTING: 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). CONCLUSION: 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.
RCT Entities:
OBJECTIVES: 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. STUDY DESIGN AND SETTING: 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). CONCLUSION: 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: Olivier Morin; Martin Vallières; Steve Braunstein; Jorge Barrios Ginart; Taman Upadhaya; Henry C Woodruff; Alex Zwanenburg; Avishek Chatterjee; Javier E Villanueva-Meyer; Gilmer Valdes; William Chen; Julian C Hong; Sue S Yom; Timothy D Solberg; Steffen Löck; Jan Seuntjens; Catherine Park; Philippe Lambin Journal: Nat Cancer Date: 2021-07-22
Authors: Nunzio Camerlingo; Martina Vettoretti; Simone Del Favero; Andrea Facchinetti; Giovanni Sparacino Journal: J Diabetes Sci Technol Date: 2020-09-17