Can preference scores for discrete states be used to derive preference scores for an entire path of events? An application to prenatal diagnosis.
The authors conducted a study exploring whether preferences for sequences of events can be approximated by preferences for component discrete states. Visual-analog-scale (VAS) and standard-gamble (SG) scores for a subset of the possible sequences of events (path states) and component temporary and chronic outcomes (discrete states) that can follow prenatal diagnostic decisions were elicited from 121 pregnant women facing a choice between chorionic villus sampling and amniocentesis. For individuals, preference scores for path states could not be predicted easily from discrete-state scores. Mean path-state VAS scores, however, were predicted reasonably accurately by multiple regression models (R2 = 0.85 and 0.82 for two different anchoring schemes), with most measured scores lying within the 95% confidence intervals of the derived scores. It is concluded that, for individual patient decision making, preferences for path states should be elicited. When mean preference values for a population are sought, however, it may be reasonable to derive regression weights from a subset of respondents and then to apply those weights to preferences for discrete states elicited from a larger group.
Kuppermann
M
M
Department of Obstetrics, Gynecology and Reproductive Sciences, University of California, San Francisco, USA. miriam.kuppermann@quickmail.ucsf.edu
Shiboski
S
S
Feeny
D
D
Elkin
E P
EP
Washington
A E
AE
D000328:Adult; D000649:Amniocentesis / Q000706:statistics & numerical data*; D015193:Chorionic Villi Sampling / Q000706:statistics & numerical data*; D002869:Chromosome Aberrations / Q000175:diagnosis / Q000517:prevention & control; D025063:Chromosome Disorders; D016001:Confidence Intervals; D003657:Decision Making; D005260:Female; D005865:Gestational Age; D006801:Humans; D007231:Infant, Newborn; D010147:Pain Measurement; D011247:Pregnancy; D011296:Prenatal Diagnosis / Q000706:statistics & numerical data*; D011788:Quality of Life; D012044:Regression Analysis; D012495:San Francisco