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Brian P Hobbs

Showing results (31-40 of 108) with videos related to

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Frontiers in Oncology|December 23, 2022
Optimal predictive probability designs for randomized biomarker-guided oncology trialsEmily C Zabor, Alexander M Kaizer, Nathan A Pennell, et al.
Bayesian Analysis|April 5, 2016
Flexible Bayesian survival modeling with semiparametric time-dependent and shape-restricted covariate effectsThomas A Murray, Brian P Hobbs, Daniel J Sargent, et al.
Health Services & Outcomes Research Methodology|May 2, 2017
Combining Non-randomized and Randomized Data in Clinical Trials Using Commensurate PriorsHong Zhao, Brian P Hobbs, Haijun Ma, et al.
Journal of Biopharmaceutical Statistics|February 7, 2022
Calibrated dynamic borrowing using capping priorsSharon X Ling, Brian P Hobbs, Alexander M Kaizer, et al.
Statistics in Medicine|December 10, 2021
Predicting outcomes of phase III oncology trials with Bayesian mediation modeling of tumor responseJie Zhou, Xun Jiang, Hong Amy Xia, et al.
Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing|December 9, 2017
Tree-based Methods for Characterizing Tumor Density HeterogeneityKatherine Shoemaker, Brian P Hobbs, Karthik Bharath, et al.
Biometrics|December 7, 2013
Semiparametric Bayesian commensurate survival model for post-market medical device surveillance with non-exchangeable historical dataThomas A Murray, Brian P Hobbs, Theodore C Lystig, et al.
Frontiers in Oncology|February 3, 2023
Landmark mediation survival analysis using longitudinal surrogateJie Zhou, Xun Jiang, H Amy Xia, et al.
Statistical Methods in Medical Research|August 27, 2021
A survival mediation model with Bayesian model averagingJie Zhou, Xun Jiang, Hong Amy Xia, et al.
Cancer Informatics|June 10, 2016
A Bayesian Nonparametric Approach for Functional Data Classification with Application to Hepatic Tissue CharacterizationKassandra M Fronczyk, Michele Guindani, Brian P Hobbs, et al.
Pageof 11

Showing results (31-40 of 108) with videos related to

Sort By:
Pageof 11
Frontiers in Oncology|December 23, 2022
Optimal predictive probability designs for randomized biomarker-guided oncology trialsEmily C Zabor, Alexander M Kaizer, Nathan A Pennell, et al.
Bayesian Analysis|April 5, 2016
Flexible Bayesian survival modeling with semiparametric time-dependent and shape-restricted covariate effectsThomas A Murray, Brian P Hobbs, Daniel J Sargent, et al.
Health Services & Outcomes Research Methodology|May 2, 2017
Combining Non-randomized and Randomized Data in Clinical Trials Using Commensurate PriorsHong Zhao, Brian P Hobbs, Haijun Ma, et al.
Journal of Biopharmaceutical Statistics|February 7, 2022
Calibrated dynamic borrowing using capping priorsSharon X Ling, Brian P Hobbs, Alexander M Kaizer, et al.
Statistics in Medicine|December 10, 2021
Predicting outcomes of phase III oncology trials with Bayesian mediation modeling of tumor responseJie Zhou, Xun Jiang, Hong Amy Xia, et al.
Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing|December 9, 2017
Tree-based Methods for Characterizing Tumor Density HeterogeneityKatherine Shoemaker, Brian P Hobbs, Karthik Bharath, et al.
Biometrics|December 7, 2013
Semiparametric Bayesian commensurate survival model for post-market medical device surveillance with non-exchangeable historical dataThomas A Murray, Brian P Hobbs, Theodore C Lystig, et al.
Frontiers in Oncology|February 3, 2023
Landmark mediation survival analysis using longitudinal surrogateJie Zhou, Xun Jiang, H Amy Xia, et al.
Statistical Methods in Medical Research|August 27, 2021
A survival mediation model with Bayesian model averagingJie Zhou, Xun Jiang, Hong Amy Xia, et al.
Cancer Informatics|June 10, 2016
A Bayesian Nonparametric Approach for Functional Data Classification with Application to Hepatic Tissue CharacterizationKassandra M Fronczyk, Michele Guindani, Brian P Hobbs, et al.
Pageof 11