Abstract
A challenge arising in cancer immunotherapy trial design is the presence of a delayed treatment effect wherein the proportional hazard assumption no longer holds true. As a result, a traditional survival trial design based on the standard log-rank test, which ignores the delayed treatment effect, will lead to substantial loss of statistical power. Recently, a piecewise weighted log-rank test is proposed to incorporate the delayed treatment effect into consideration of the trial design. However, because the sample size formula was derived under a sequence of local alternative hypotheses, it results in an underestimated sample size when the hazard ratio is relatively small for a balanced trial design and an inaccurate sample size estimation for an unbalanced design. In this article, we derived a new sample size formula under a fixed alternative hypothesis for the delayed treatment effect model. Simulation results show that the new formula provides accurate sample size estimation for both balanced and unbalanced designs.
Original language | English |
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Pages (from-to) | 202-213 |
Number of pages | 12 |
Journal | Pharmaceutical Statistics |
Volume | 19 |
Issue number | 3 |
DOIs | |
State | Published - May 1 2020 |
Bibliographical note
Funding Information:We thank the referees for their many constructive comments that have led to significant improvements in the article. This research was supported by the Biostatistics and Bioinformatics Shared Resource Facility of the University of Kentucky Markey Cancer Center (P30CA177558).
Funding Information:
We thank the referees for their many constructive comments that have led to significant improvements in the article. This research was supported by the Biostatistics and Bioinformatics Shared Resource Facility of the University of Kentucky Markey Cancer Center (P30CA177558).
Publisher Copyright:
© 2019 John Wiley & Sons, Ltd.
Keywords
- cancer clinical trial
- delayed treatment effect
- piecewise weighted log-rank test
- sample size
ASJC Scopus subject areas
- Statistics and Probability
- Pharmacology
- Pharmacology (medical)