Quality-adjusted life year weights and treatment bias: Theory and evidence from cognitive interviews

The purpose of this research is to understand the thought processes that underpin responses to stated preference approaches for eliciting quality of life, in particular the standard gamble.

We utilize standard gamble preference elicitation survey techniques to elicit quality-adjusted life year weights for two reduced health states: chronic severe depression and total blindness. After the survey, we conduct open-ended qualitative interviews with respondents to determine their thought processes while taking the surveys and to shed light on what their quality-adjusted life year weight is capturing. Survey responses were coded and analyzed for themes in NVivo, the results of which were then formalized in the terminology of decision sciences.

The qualitative results of the cognitive interviews present systematic evidence for a type of cognitive bias present in standard gamble quality-adjusted life year weight elicitation, which has not been previously highlighted and which we call treatment bias. We define this treatment bias as the consideration of salient treatment alternatives correlated with a reduced health state, when these alternatives are not explicitly posed in the question. Our formalization of this cognitive behavior demonstrates that treatment bias will always bias the elicited health state utility of treating the illness in question downward.

The treatment bias highlighted in this study has implications for economic evaluation when comparing treatment for illnesses where alternative treatments are widely publicized versus those that are not. For example, comparing the effectiveness of treating depression versus arthritis may be biased against depression if advertisements for anti-depressants are more widely viewed by survey respondents than advertisements for arthritis treatments. We propose a statement to be imbedded in all questionnaires regarding stated preference elicitation of quality-adjusted life year weights in order to correct for this bias in future stated preference surveys.