HARNESSING A LARGE OBSERVATIONAL DATABASE TO IMPROVE PREDICTIONS FROM A STATED PREFERENCE SURVEY: THE CASE OF BLOOD DONATION

Purpose:

Stated preference (SP) methods elicit individuals’ preferences and are widely used in health policy and clinical decision-making. A major concern is that individuals’ responses to hypothetical choices may not reflect their real preferences, which brings into question the external validity of the stated preferences. Recent research has found that external validity from SP surveys is lower for publicly- versus privately- funded goods, and those with a moral component. Moreover, in a health context, the few SP studies that have assessed external validity, have reported aggregate differences between predictions from SP data and revealed preferences. The aim of this paper is to contrast the predictions from a SP survey with the revealed preferences observed in a large, linked observational dataset.

Method:

This paper uses a case study of blood donation to illustrate an approach for improving the accuracy of predictions from SP models. A large online SP survey (5000 invitees) was administered to provide information about donors’ willingness to donate blood at different frequencies, according to alternative future policy options. Donors invited to complete the SP survey were selected from a large longitudinal dataset, the PULSE database, of all 1.2 million blood donors in England, which records the frequency with which donors actually donate blood under current policies. For those policies, we contrasted the actual donation frequency with the same donors’ predicted donation frequency estimated with a multinominal logit model. This ‘within sample approach’ to estimating the discrepancy between stated and revealed preferences minimizes bias due to unobserved confounding.

Result:

Compared to the observed frequencies of blood donation, the SP model overestimated the frequency by 31% for women and 40% for men, with wider differences in the estimated discrepancies according to other characteristics. For example, the predicted and observed frequencies differ by 45% for younger women (aged 17-30), but only by 16% for older women (aged 61-70).

Conclusion:

This approach can extend the external validity of SP models by harnessing large data to calibrate predictions at the level of the subgroup rather than at the aggregate level. Hence, this method can help to improve the predictive value of responses to SP surveys, and their usefulness for decision-making.