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For: Ohwada S, Morita S. Bayesian adaptive patient enrollment restriction to identify a sensitive subpopulation using a continuous biomarker in a randomized phase 2 trial. Pharmaceut Statist 2016;15:420-9. [DOI: 10.1002/pst.1761] [Cited by in Crossref: 4] [Cited by in F6Publishing: 2] [Article Influence: 0.7] [Reference Citation Analysis]
Number Citing Articles
1 Du Y, Chen H, Varadhan R. Lasso estimation of hierarchical interactions for analyzing heterogeneity of treatment effect. Stat Med 2021. [PMID: 34240443 DOI: 10.1002/sim.9132] [Reference Citation Analysis]
2 Vinnat V, Chevret S. Enrichment Bayesian design for randomized clinical trials using categorical biomarkers and a binary outcome. BMC Med Res Methodol 2022;22. [DOI: 10.1186/s12874-022-01513-z] [Reference Citation Analysis]
3 Wang T, Wang X, George SL, Zhou H. Design and analysis of biomarker-integrated clinical trials with adaptive threshold detection and flexible patient enrichment. J Biopharm Stat 2020;30:1060-76. [PMID: 33175640 DOI: 10.1080/10543406.2020.1832110] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 0.5] [Reference Citation Analysis]
4 Bertsche A, Fleischer F, Beyersmann J, Nehmiz G. Bayesian Phase II optimization for time-to-event data based on historical information. Stat Methods Med Res 2019;28:1272-89. [PMID: 29284369 DOI: 10.1177/0962280217747310] [Cited by in Crossref: 2] [Cited by in F6Publishing: 2] [Article Influence: 0.4] [Reference Citation Analysis]
5 Liu Y, Kairalla JA, Renfro LA. Bayesian adaptive trial design for a continuous biomarker with possibly nonlinear or nonmonotone prognostic or predictive effects. Biometrics 2021. [PMID: 34415052 DOI: 10.1111/biom.13550] [Reference Citation Analysis]
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