Transl Clin Pharmacol.  2023 Dec;31(4):202-216. 10.12793/tcp.2023.31.e21.

Adaptive design clinical trials: current status by disease and trial phase in various perspectives

• Lee H
• Hwang S
• Jang IJ
• Chung JY
• Oh J

• KMID: 2552441
• DOI: http://doi.org/10.12793/tcp.2023.31.e21

Abstract

An adaptive design is a clinical trial design that allows for modification of a structured plan in a clinical trial based on data accumulated during pre-planned interim analyses. This flexible approach to clinical trial design improves the success rate of clinical trials while reducing time, cost, and sample size compared to conventional methods. The purpose of this study is to identify the current status of adaptive design and present key considerations for planning an appropriate adaptive design based on specific circumstances. We searched for clinical trials conducted between January 2006 to July 2021 in the Clinical Trials Registry (ClinicalTrials.gov) using keywords specified in the Food and Drug Administration Adaptive Design Clinical Trial Guidelines. In order to analyze the adaptive designs used in selected cases, we classified the results according to the phase of the clinical trial, type of indication, and the specific adaptation method employed. A total of 267 clinical trials were identified on ClinicalTrials.gov. Among them, 236 clinical trials actually applied adaptive designs and were classified according to phase, indication types, and adaptation methods. Adaptive designs were most frequently used in phase 2 clinical trials and oncology research. The most commonly used adaptation method was the adaptive treatment selection design. In the case of coronavirus disease 2019, the most frequently used designs were adaptive platform design and seamless design. Through this study, we expect to provide valuable insights and considerations for the implementation of adaptive design clinical trials in different diseases and stages.

Keyword

Adaptive Clinical Trial Design; Early Termination of Clinical Trials; Flexibility; Cost Effectiveness; Data Adjustment; Bayesian Analysis