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William F. Rosenberger
Researcher at George Mason University
Publications - 147
Citations - 5367
William F. Rosenberger is an academic researcher from George Mason University. The author has contributed to research in topics: Restricted randomization & Randomization. The author has an hindex of 36, co-authored 145 publications receiving 4956 citations. Previous affiliations of William F. Rosenberger include University of Maryland, Baltimore & George Washington University.
Papers
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Journal ArticleDOI
Comparison of open and laparoscopic live donor nephrectomy.
John L. Flowers,Stephen C. Jacobs,Eugene Cho,Andrew C. Morton,William F. Rosenberger,Deborah Evans,Anthony L. Imbembo,Stephen T. Bartlett +7 more
TL;DR: Laroscopic live donor nephrectomy can be performed with morbidity and mortality comparable to open donor ne phrectomy, with substantial improvements in patient recovery after the laparoscopic approach.
Book
Randomization in Clinical Trials: Theory and Practice
TL;DR: In this paper, the effects of bias bias bias on the allocation of treatment allocation in clinical trials are discussed, including the effect of unobserved covariates Selection bias Randomization as a Basis for Inference Inference for Stratified, Blocked, and Covariate-Adjusted Analyses Randomization in Practice Response-Adaptive Randomization Inference For Response Adaptive Rondomization Response Adaptation Randomization is used in Clinical Trials.
Journal ArticleDOI
Optimal Adaptive Designs for Binary Response Trials
William F. Rosenberger,William F. Rosenberger,Nigel Stallard,Anastasia Ivanova,Cherice N. Harper,Michelle L. Ricks +5 more
TL;DR: It is found that the sequential procedure generally results in fewer treatment failures than the other procedures, particularly when the success probabilities of treatments are smaller.
Book
The Theory of Response-Adaptive Randomization in Clinical Trials
TL;DR: In this article, the authors present a general framework for response-adaptive randomization in clinical trials and prove the main theorems of the general framework in terms of power, probability, and asymptotic properties.
Journal ArticleDOI
Optimality, Variability, Power
TL;DR: In this paper, a Taylor expansion of the noncentrality parameter of the usual chi-squared test for binary responses is used to compare different response-adaptive randomization procedures and different target allocations in terms of power and expected treatment failure rate.