Skip to main navigation
Skip to search
Skip to main content
Experts@Minnesota Home
Home
Profiles
Research units
University Assets
Projects and Grants
Research output
Datasets
Press/Media
Activities
Fellowships, Honors, and Prizes
Impacts
Search by expertise, name or affiliation
Bayesian adaptive design for device surveillance
Thomas A. Murray
, Bradley P. Carlin
, Theodore C. Lystig
Biostatistics
Research output
:
Contribution to journal
›
Article
›
peer-review
Overview
Fingerprint
Fingerprint
Dive into the research topics of 'Bayesian adaptive design for device surveillance'. Together they form a unique fingerprint.
Sort by
Weight
Alphabetically
Keyphrases
Device Surveillance
100%
Bayesian Adaptive Design
100%
Maximum Sample Size
66%
Medical Devices
33%
Postmarket
33%
Post-marketing Surveillance
33%
Guidance Documents
33%
Surveillance Studies
33%
Proposed Design
33%
Adaptive Framework
33%
Model Change
33%
Model Misspecification
33%
Operating Characteristics
33%
Survival Function
33%
Non-adaptive
33%
Enrolment Rates
33%
Method Performance
33%
Desired Power
33%
Bayesian Adaptive
33%
Trial Enrollment
33%
Current Group
33%
All-at-once
33%
Efficient Framework
33%
Frequentist Methods
33%
Computer Science
Guidance Document
100%
Primary Objective
100%
Surveillance Study
100%
Mathematics
Bayesian
100%
Survival Function
25%
Frequentist Method
25%
Primary Objective
25%
Model Misspecification
25%
Desired Power
25%