Detecting Heterogeneity of Intervention Effects in Comparative Judgments

Comparative measures such as paired comparisons and rankings are frequently used to evaluate health states and quality of life. The present article introduces log-linear Bradley-Terry (LLBT) models to evaluate intervention effectiveness when outcomes are measured as paired comparisons or rankings and presents a combination of the LLBT model and model-based recursive partitioning (MOB) to detect treatment effect heterogeneity. The MOB LLBT approach enables researchers to identify subgroups that differ in the preference order and in the effect an intervention has on choice behavior. Applicability of MOB LLBT models is demonstrated using an artificial data example with known data-generating mechanism and a real-world data example focusing on drug-harm perception among music festival visitors. In the artificial data example, the MOB LLBT model is able to adequately recover the true (population) model. In the real-world data example, the standard LLBT model confirms the existence of a situational willingness among festival visitors to trivialize drug harm when peer consumption behavior is made cognitively accessible. In addition, MOB LLBT results suggest that this trivialization effect is highly context-dependent and most pronounced for participants with low-to-moderate alcohol intoxication who also proactively contacted a substance counselor at the festival venue. Both data examples suggest that MOB LLBT models allow for more nuanced statements about the effectiveness of interventions. We provide R code examples to implement MOB LLBT models for paired comparisons, rankings, and rating (Likert-type) data.

Automated summary

The article introduces log-linear Bradley-Terry (LLBT) models to evaluate health states and quality of life using comparative measures such as paired comparisons and rankings. It also presents a combination of the LLBT model and model-based recursive partitioning (MOB) to detect treatment effect heterogeneity. The MOB LLBT approach allows researchers to identify subgroups that differ in preference order and in the effect of an intervention on choice behavior. The applicability of MOB LLBT models is demonstrated using artificial and real-world data examples, showing their ability to provide nuanced statements about the effectiveness of interventions.