Advanced Search

Journal Navigation

Journal Home

Subscriptions

Archive

Contact Us

Table of Contents

SAGETRACK

Sign In to gain access to subscriptions and/or personal tools.
Medical Decision Making
This Article
Right arrow Free Full Text (Free PDF) Free
Right arrow References
Right arrow Alert me when this article is cited
Right arrow Alert me if a correction is posted
Right arrow Citation Map
Services
Right arrow Email this article to a friend
Right arrow Similar articles in this journal
Right arrow Similar articles in Web of Science
Right arrow Similar articles in PubMed
Right arrow Alert me to new issues of the journal
Right arrow Add to Saved Citations
Right arrow Download to citation manager
Right arrowRequest Permissions
Right arrow Request Reprints
Right arrow Add to My Marked Citations
Citing Articles
Right arrow Citing Articles via HighWire
Right arrow Citing Articles via Web of Science (3)
Right arrow Citing Articles via Google Scholar
Right arrow Citing Articles via Scopus
Google Scholar
Right arrow Articles by Krabbe, P. F. M.
Right arrow Search for Related Content
PubMed
Right arrow PubMed Citation
Right arrow Articles by Krabbe, P. F. M.
Social Bookmarking
Add to CiteULike   Add to Complore   Add to Connotea   Add to Del.icio.us   Add to Digg   Add to Reddit   Add to Technorati   Add to Twitter  
What's this?

Valuation Structures of Health States Revealed with Singular Value Decomposition

Paul F. M. Krabbe, Phd

Department of Medical Technology Assessment (138), Radboud University Nijmegen Medical Centre, PO Box 9101, 6500 HB Nijmegen, the Netherlands; p.krabbe{at}mta.umcn.nl

Objective. A basic mathematical routine called singular value decomposition (SVD) is introduced and applied to explore the applicability of this methodology in the context of health state valuations. Methods. SVD dissects a data matrix into 3 separatematrices that contain all the information present in the original data. Eachmatrix comprises a specific type of information. One matrix comprises arrays of weights that show the different valuation structures (i.e., similar ways among respondents to quantify specific sets of health states). A 2nd matrix with weights expresses how strongly each respondent's ratings are related to each of the valuation structures, and a 3rd matrix contains the percentages of variance associated with the valuation structures. SVD was applied to data from a group of 340 respondents who each gave a value to 16 health states using the time tradeoff (TTO) method and the visual analog scale (VAS). Results. SVD of the VAS data showed 1 distinct response pattern that accounted for 91.6% of the total variance. The contribution of the 1st component in the TTO data wasmuch lower (57.4%), and a 2nd component (15.6%) could be identified that reflected a distinct preference structure opposed to the 1st and principal component. Conclusions. Application of SVD to the TTO data revealed that respondents fell into 2 different groups in their TTO evaluations, but respondents weremore similar to each other in their VAS responses. The author discusses other applications of SVD to clinical research.

Key Words: health states • valuations • values • utilities • preferences • valuation techniques • time tradeoff • visual analog scale • EQ-5D

Medical Decision Making, Vol. 26, No. 1, 30-37 (2006)
DOI: 10.1177/0272989X05284106
Add to CiteULike CiteULike   Add to Complore Complore   Add to Connotea Connotea   Add to Del.icio.us Del.icio.us   Add to Digg Digg   Add to Reddit Reddit   Add to Technorati Technorati   Add to Twitter Twitter    What's this?


This article has been cited by other articles:


Home page
 Med Decis MakingHome page
P. F. M. Krabbe, J. A. Salomon, and C. J. L. Murray
Quantification of Health States with Rank-Based Nonmetric Multidimensional Scaling
Med Decis Making, August 1, 2007; 27(4): 395 - 405.
[Abstract] [PDF]