An adaptive design for early clinical development including interim decision for single‐arm trial with external controls or randomized trial. (21st January 2022)

• Record Type: Journal Article
• Authors: Götte, Heiko; Kirchner, Marietta; Krisam, Johannes; Allignol, Arthur; Lamy, Francois‐Xavier; Schüler, Armin; Kieser, Meinhard
• Is Part Of: Pharmaceutical statistics. Volume 21:Number 3(2022)
• Page Start: 625
• Page End: 640
• Publication Date: 2022-01-21
• Subjects: confounder adjustment -- loss function -- preference score -- proof‐of‐concept -- real‐world data; Pharmacy -- Statistical methods -- Periodicals; Pharmacy -- Statistics -- Periodicals; 615.10727
• DOI: 10.1002/pst.2190
• Languages: English
• ISSNs: 1539-1604

Abstract

Abstract: In early clinical development, randomized controlled trials (RCT) or single‐arm trials with external controls (SATwEC) are design options, which allow adjustment for confounding: RCT via design, SATwEC via analysis using propensity score methods. SATwEC requires less investment than RCT. However, if the confounder space substantially differs between the experimental and external control group, the SATwEC might lead to inappropriate decisions for further development. We develop an adaptive two‐stage design (ATD) for early clinical development that reduces the risk of unreliable decision‐making at the end of a SATwEC. In Stage I, subjects are solely assigned to the experimental group. If at the interim the propensity score distributions of internal and external data are comparable based on the preference score, the subjects in stage II will again be solely assigned to the experimental arm; if not, a randomized stage II will be conducted. In a simulation study guided by a motivating example, data is generated using a time‐to‐event model with observable and unobservable confounders. The confounder space is varied to investigate the impact on false go/stop probabilities as well as a loss function, which reflects the quality of treatment effect estimates and decision‐making. The proposed ATD provides a compromise between optimizing quality (as expressed by false go/stop probabilities and the loss function) and investment (defined by sample size and trial duration).