Designing a randomized trial with sample-size re-estimation: Challenges and Best Practices

LEARN ON SAMPLE-SIZE RE-ESTIMATION, CONDITIONAL POWER AND THE PROMISING-ZONE METHOD. 

LISTEN TO THE RECORDED WEBINAR 

KEY TAKEWAYS 

During this webinar, the participant will: 

  • Be exposed to the concepts of sample-size re-estimation, conditional power, and the promising-zone method 
  • Gain insights into the planning of sample-size re-estimation and the operationalization of such designs  
  • Understand the benefits and limitations of sample size re-estimation in relation to the clinical setting in which the study is conducted 

In this webinarwe will play the part of a sponsor consulting a statisticianWebinar banner March 25 with the goal of designing a randomized trial with sample-size re-estimationWe will discuss the concept of conditional power and the promising-zone method, and we will present a pragmatic view of some issues, including when to plan the sample-size re-estimation in the course of the accrual, how to select the amount of sample-size increase, and how to operate such trials in practice. We will also discuss the pros- and cons- of using this type of design in relation to the clinical setting in which the trial is designed and endpoints it uses. Moreover, we will discuss the regulators’ views and illustrate our discussion with practical examples.

It is often the case in drug development, particularly in oncology, that a randomized trial is launched with considerable uncertainty about the final sample size required to demonstrate a clinically meaningful and statistically significant improvement in results over the control treatment. Group-sequential designs have an established role in randomized trials and allow for safety monitoring as well as early decisions regarding efficacy and futility of the new treatment. In some trials, it may be interesting to foresee the possibility of adapting the final sample size if results at an interim analysis suggest the possibility of a positive trial, albeit with a less pronounced treatment effect than initially anticipated. Such adaptive designs that allow for sample-size re-estimation have gained traction in recent years. However, they pose several issues that require consideration and advanced statistical input.  

SPEAKERS

Laurence Collette, MSc, PhDPrincipal Statistician, Consulting Services at the International Drug Development Institute (IDDI). Laurence Collette, MSc, PhD: Principal Statistician, Consulting Services at IDDI

 

  • She has 25 years of experience in design, analysis and publication of clinical trials in oncology 
  • She graduated in Mathematics at the Université Catholique de Louvain, Louvain-la-Neuve, and in Biostatistics at the Hasselt University, in Belgium, and did her PhD at the Erasmus University in Rotterdam, The Netherlands  
  • After 25 years developing cancer treatments mostly through late phase trials with the European Organisation for Research and Treatment of Cancer (EORTC), she joined IDDI to develop and apply her expertise to earlier development phases. She has a special interest in large pragmatic clinical trials, and in adaptive designs, when they bring efficiency gains that outperform their extra complexity. 

 Everardo Saad, MDMedical Director at the International Drug Development Institute (IDDI).  Everardo Saad, MD: Medical Director at IDDI

 

  • He has nearly 20 years of experience in Medical Oncology and clinical-trial design 
  • He graduated in Medicine and trained in Internal Medicine in Sao Paulo, and did his fellowship in Medical Oncology at the University of Texas M.D. Anderson Cancer Center, in Houston.  
  • After practicing for several years, he shifted his professional career towards education and research in Medical Oncology, and has a special interest in clinical trial methodology, the estimation of endpoints, and the development of novel therapies for cancer patients. 
Presentation Webinar

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