An in silico approach helped to identify the best experimental design, population, and outcome for future randomized clinical trials

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• 作者：Agathe Bajard^a ; Sylvie Chabaud^a ; ^{sylvie.chabaud@lyon.unicancer.fr" class="auth_mail" title="E-mail the corresponding author} ; Catherine Cornu^b ; ^c ; ^d ; Anne-Charlotte Castellan^d ; Salma Malik^b ; ^c ; ^d ; Polina Kurbatova^c ; ^e ; Vitaly Volpert^e ; Nathalie Eymard^e ; Behrouz Kassai^b ; ^c ; ^d ; Patrice Nony^b ; ^c ; ; the CRESim & Epi-CRESim study groups
• 关键词：Randomized controlled clinical trial ; Simulation ; Modeling ; Experimental design ; Statistical analysis ; Therapeutic evaluation
• 刊名：Journal of Clinical Epidemiology
• 出版年：2016
• 出版时间：January 2016
• 年：2016
• 卷：69
• 期：Complete
• 页码：125-136
• 全文大小：1400 K

文摘

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.