ArticleContemporary clinical trials2025
Assessing the role of correlation between efficacy and toxicity endpoints in the performance of Bayesian optimal phase II design.
Article in Contemporary clinical trials, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
Abstract
Bayesian optimal phase II (BOP2) design serves to screen the efficacy and/or toxicity of a new treatment, determining whether it warrants further development. When employing the BOP2 design to jointly monitor the efficacy-toxicity tradeoff, the correlation between them plays an important role in achieving good performance on the statistical power while controlling Type I error. However, in practice, this correlation is usually unknown, posing challenges in the trial design and data analysis. In this study, the phi coefficient is chosen to measure the correlation. The influence of the efficacy-toxicity tradeoff on the power of BOP2 designs is first evaluated in the design stage and the data analysis stage, separately, then, overall considerations are given. In the design stage, we observe that the power increases as phi increases. Upon performing the sensitivity analysis in the data analysis stage, we find that the power decreases as phi increases given the pre-determined stopping boundaries. Simulations demonstrate instances of overpowering with inflated Type I error when the assumed phi in the design stage exceeds the true phi in the data analysis stage. Conversely, underpowering occurs with controlled Type I error when the assumed phi is lower than the true phi in the data analysis stage. To obtain the power under controlled Type I error in design, we recommend the use of independent correlation when the efficacy and toxicity are likely to be positively correlated. Conversely, for cases of likely negative correlation, taking a phi value close to the lower bound is advisable.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.