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.