Interim Analysis in Clinical Trials

John A. Kairalla (), Rachel Zahigian () and Samuel S. Wu ()
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John A. Kairalla: University of Florida
Rachel Zahigian: Vertex Pharmaceuticals
Samuel S. Wu: University of Florida

Chapter 59 in Principles and Practice of Clinical Trials, 2022, pp 1083-1102 from Springer

Abstract: Abstract Modern randomized controlled trials often involve multiple periods of data collection separated by interim analyses, where the accumulated data is analyzed and findings are used to make adjustments to the ongoing trial. Various endpoints can be used to influence these decisions, including primary or surrogate outcome data, safety data, administrative data, and/or new external information. Example uses of interim analyses include deciding if there is evidence that a trial should be stopped early for safety, efficacy, or futility or if the treatment allocation ratios should be modified to optimize trial efficiency and better align the risk-benefit ratio. Additionally, a decision could be made to lengthen or shorten a trial based on observed information. To avoid unwanted bias, studies known as adaptive design clinical trials pre-specify these decision rules in the study protocol. Extensive simulation studies are often required during study planning and protocol development in order to characterize operating characteristics and validate testing procedures and parameter estimation. Over time, researchers have gained a better understanding of the strengths and limitations of employing interim analyses in their clinical studies. In particular, with proper planning and conduct, adaptive designs incorporating interim analyses can provide great benefits in flexibility and efficiency. However, an increase in infrastructure for development and planning is needed to successfully implement adaptive designs and interim analyses and allow their potential advantages to be achieved in clinical research.

Keywords: Adaptive design; Early stopping; Flexible design; Futility; Interim analysis; Interim monitoring; Group sequential; Safety monitoring; Nuisance parameter; Sample size (search for similar items in EconPapers)
Date: 2022
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DOI: 10.1007/978-3-319-52636-2_84

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