TY - JOUR
T1 - Sample size re-estimation in adaptive enrichment design
AU - Lin, Ruitao
AU - Yang, Zhao
AU - Yuan, Ying
AU - Yin, Guosheng
N1 - Funding Information:
We thank the two referees, Associate Editor, and Editor for their many constructive and insightful comments that have led to significant improvements in the article. Lin's research was supported in part by grants P30 CA016672 and P50 CA221703 from the National Cancer Institute (NCI), Yuan's research was supported in part by grants P50 CA098258 and P30 CA016672 from the NCI, and Yin's research was supported in part by a grant No. 17308420 from the Research Grants Council of Hong Kong .
Funding Information:
We thank the two referees, Associate Editor, and Editor for their many constructive and insightful comments that have led to significant improvements in the article. Lin's research was supported in part by grants P30 CA016672 and P50 CA221703 from the National Cancer Institute (NCI), Yuan's research was supported in part by grants P50 CA098258 and P30 CA016672 from the NCI, and Yin's research was supported in part by a grant No. 17308420 from the Research Grants Council of Hong Kong.
Publisher Copyright:
© 2020 Elsevier Inc.
PY - 2021/1
Y1 - 2021/1
N2 - Clinical trial participants are often heterogeneous, which is a fundamental problem in the rapidly developing field of precision medicine. Participants heterogeneity causes considerable difficulty in the current phase III trial designs. Adaptive enrichment designs provide a flexible and intuitive solution. At the interim analysis, we enrich the subgroup of trial participants who have a higher likelihood to benefit from the new treatment. However, it is critical to control the level of the test size and maintain adequate power after enrichment of certain subgroup of participants. We develop two adaptive enrichment strategies with sample size re-estimation and verify their feasibility and practicability through extensive simulations and sensitivity analyses. The simulation studies show that the proposed methods can control the overall type I error rate and exhibit competitive improvement in terms of statistical power and expected sample size. The proposed designs are exemplified with a real trial application.
AB - Clinical trial participants are often heterogeneous, which is a fundamental problem in the rapidly developing field of precision medicine. Participants heterogeneity causes considerable difficulty in the current phase III trial designs. Adaptive enrichment designs provide a flexible and intuitive solution. At the interim analysis, we enrich the subgroup of trial participants who have a higher likelihood to benefit from the new treatment. However, it is critical to control the level of the test size and maintain adequate power after enrichment of certain subgroup of participants. We develop two adaptive enrichment strategies with sample size re-estimation and verify their feasibility and practicability through extensive simulations and sensitivity analyses. The simulation studies show that the proposed methods can control the overall type I error rate and exhibit competitive improvement in terms of statistical power and expected sample size. The proposed designs are exemplified with a real trial application.
KW - Conditional power
KW - Enrichment strategies
KW - Patient heterogeneity
KW - Phase III trial designs
KW - Sample size re-estimation
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U2 - 10.1016/j.cct.2020.106216
DO - 10.1016/j.cct.2020.106216
M3 - Article
C2 - 33246098
AN - SCOPUS:85097746568
SN - 1551-7144
VL - 100
JO - Contemporary Clinical Trials
JF - Contemporary Clinical Trials
M1 - 106216
ER -