2018
DOI: 10.1002/sim.7897
|View full text |Cite
|
Sign up to set email alerts
|

A practical Bayesian adaptive design incorporating data from historical controls

Abstract: In this paper, we develop the fixed-borrowing adaptive design, a Bayesian adaptive design which facilitates information borrowing from a historical trial using subject-level control data while assuring a reasonable upper bound on the maximum type I error rate and lower bound on the minimum power. First, one constructs an informative power prior from the historical data to be used for design and analysis of the new trial. At an interim analysis opportunity, one evaluates the degree of prior-data conflict. If th… Show more

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...
2
1
1
1

Citation Types

1
22
0

Year Published

2019
2019
2024
2024

Publication Types

Select...
7

Relationship

0
7

Authors

Journals

citations
Cited by 25 publications
(23 citation statements)
references
References 19 publications
1
22
0
Order By: Relevance
“…For example, if an interim analysis suggests that the external information is not relevant, more patients may be randomized to control after the interim analysis. 62,63…”
Section: External Control Information In Rctsmentioning
confidence: 99%
See 1 more Smart Citation
“…For example, if an interim analysis suggests that the external information is not relevant, more patients may be randomized to control after the interim analysis. 62,63…”
Section: External Control Information In Rctsmentioning
confidence: 99%
“…Additionally, adaptive trial designs, such as Bayesian group sequential designs, may help to reduce the chance of inconclusive results due to conflicting trial external information. For example, if an interim analysis suggests that the external information is not relevant, more patients may be randomized to control after the interim analysis …”
Section: External Control Information In Rctsmentioning
confidence: 99%
“…Many models, such as hierarchical models and the power prior, are based on the assumption of exchangeability. As pointed out by Psioda et al., 35 we prefer to see historical data that give the prior as non-random. In this point of view, the prior simply reflects the previous knowledge, which can be near the truth or not with respect to the new prospective trial.…”
Section: Discussionmentioning
confidence: 99%
“…proposed using a fixed power prior that accommodates patient‐specific covariates to more closely align the two control groups. In this case, the amount of borrowing would be fixed and determined a priori , but in the case of discord between current and historic data the analysis can revert to a noninformative prior 85 . Neuenschwander et al .…”
Section: Current Statistical Methods For Combining Current Controls Amentioning
confidence: 99%
“…In this case, the amount of borrowing would be fixed and determined a priori, but in the case of discord between current and historic data the analysis can revert to a noninformative prior. 85 Neuenschwander et al proposed a modified version of the power prior, which estimates the value of the power parameter from available data while accommodating the uncertainty about the parameter. 86 The resulting posterior is analogous to a weighted average of the historic and current data.…”
Section: Reviewmentioning
confidence: 99%