Vance W. Berger
University of Maryland, Baltimore County
140 Papers
856 Citations
Vance W. Berger is an academic researcher from University of Maryland, Baltimore County. The author has contributed to research in topics: Randomized controlled trial & Selection bias. The author has an hindex of 24, co-authored 133 publications. Previous affiliations of Vance W. Berger include Food and Drug Administration & National Institutes of Health.
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Papers
A general framework for the evaluation of clinical trial quality.
TL;DR: An adequate evaluation of clinical quality would need to enumerate all known biases, update this list periodically, score the trial with regard to each potential bias on a scale of 0% to 100%, offer partial credit for only that which can be substantiated, and then multiply (not add) the component scores to obtain an overall score between 0% and 100%.
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•Book
Selection Bias and Covariate Imbalances in Randomized Clinical Trials
Vance W. Berger
- 27 May 2005
195
Detecting Selection Bias in Randomized Clinical Trials
Vance W. Berger,Derek V. Exner +1 more
TL;DR: A new method for detecting selections bias, based on response data only, is developed for the case in which a small block size, and either unmasking of treatment codes or an open-label design, have compromised the concealment of allocation.
148
A roadmap to using randomization in clinical trials.
Vance W. Berger,L.J. Bour,Kerstine Carter,Jonathan Chipman,Colin C Everett,Nicole Heussen,Catherine Hewitt,Ralf-Dieter Hilgers,Yuqun Abigail Luo,Jone Renteria,Yevgen Ryeznik,Oleksandr Sverdlov,Diane Uschner +12 more
TL;DR: Restricted randomization procedures targeting 1:1 allocation are found to be robust and valid alternatives to likelihood-based tests and should be considered more frequently by clinical investigators.
Exact inference for growth curves with intraclass correlation structure.
TL;DR: This paper will demonstrate that exact inference is possible using generalized inference on regression coefficients when an intraclass correlation structure is assumed and is demonstrated to be possible using Bayesian inference.
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