Seamless phase 2/3 design for trials with multiple co-primary endpoints using Bayesian predictive power

• Published in: BMC medical research methodology Vol. 24; no. 1; pp. 12 - 13
• Main Authors: Yang, Jiaying, Li, Guochun, Yang, Dongqing, Wu, Juan, Wang, Junqin, Gao, Xingsu, Liu, Pei
• Format: Journal Article
• Language: English
• Published: England BioMed Central Ltd 17.01.2024; BioMed Central; BMC
• Subjects: Bayes Theorem; Bayesian predictive power; Bayesian statistical decision theory; Binomial distribution; Clinical trials; Co-primary endpoints; Conditional power; Experimental design; Humans; Hypotheses; Medical Futility; Medical research; Methods; Probability; Research Design; Response rates; Sample Size; Seamless phase 2/3 design; Success; Vaccines; China; Bayesian predictive power; Co-primary endpoints; Seamless phase 2/3 design; Conditional power
• ISSN: 1471-2288; 1471-2288
• DOI: 10.1186/s12874-024-02144-2

Seamless phase 2/3 design has become increasingly popular in clinical trials with a single endpoint. Trials that define success based on the achievement of all co-primary endpoints (CPEs) encounter the challenge of inflated type 2 error rates, often leading to an overly large sample size. To tackle this challenge, we introduced a seamless phase 2/3 design strategy that employs Bayesian predictive power (BPP) for futility monitoring and sample size re-estimation at interim analysis. The correlations among multiple CPEs are incorporated using a Dirichlet-multinomial distribution. An alternative approach based on conditional power (CP) was also discussed for comparison. A seamless phase 2/3 vaccine trial employing four binary endpoints under the non-inferior hypothesis serves as an example. Our results spotlight that, in scenarios with relatively small phase 2 sample sizes (e.g., 50 or 100 subjects), the BPP approach either outperforms or matches the CP approach in terms of overall power. Particularly, with n 1  = 50 and ρ  = 0, BPP showcases an overall power advantage over CP by as much as 8.54%. Furthermore, when the phase 2 stage enrolled more subjects (e.g., 150 or 200), especially with a phase 2 sample size of 200 and ρ  = 0, the BPP approach evidences a peak difference of 5.76% in early stop probability over the CP approach, emphasizing its better efficiency in terminating futile trials. It’s noteworthy that both BPP and CP methodologies maintained type 1 error rates under 2.5%. In conclusion, the integration of the Dirichlet-Multinominal model with the BPP approach offers improvement in certain scenarios over the CP approach for seamless phase 2/3 trials with multiple CPEs.