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A latent class method for the selection of prototypes using expert ratings.

Authors
Miller-WE
Source
Stat Med 2012 Jan; 30(1):80-92
NIOSHTIC No.
20040245
Abstract
Latent class analysis can be applied to the outcomes of expert ratings to select objects or subjects that are regarded as prototypical of a category in an ordinal classification system. During a pilot study, Monte Carlo simulations demonstrated that the probability of correct selection is larger when using latent class analysis than when usingmethods that rely on agreement statistics. Further improvements in the latent class results can also be achieved by applying affine transformations to latent class estimates of sensitivity and specificity. An application is presented that involves the selection of prototypical radiographs.
Keywords
Statistical-analysis; Qualitative-analysis; Simulation-methods; Surveillance; Author Keywords: Monte Carlo simulation; methodology; affine transformation; ordinal classification; random block design
Contact
William E. Miller, Division of Respiratory Disease Studies, National Institute for Occupational Safety and Health (NIOSH), Centers for Disease Control and Prevention (CDC), 1095 Willowdale Rd., Morgantown,WV 26505-2888, USA
CODEN
SMEDDA
Publication Date
20120101
Document Type
Journal Article
Email Address
wem0@cdc.gov
Fiscal Year
2012
NTIS Accession No.
NTIS Price
Identifying No.
B02172012
Issue of Publication
1
ISSN
0277-6715
NIOSH Division
DRDS
Source Name
Statistics in Medicine
State
WV
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