Valuation structures of health states revealed with singular value decomposition.
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.
Department of Medical Technology Assessment (138), Radboud University Nijmegen Medical Centre, PO Box 9101, 6500 HB Nijmegen, the Netherlands. p.krabbe@mta.umcn.nl
Paul F M
PF
Krabbe
D003625:Data Collection; D006304:Health Status; D006801:Humans; D015233:Models, Statistical; D010147:Pain Measurement; D006113:United Kingdom; D014481:United States