This study examined whether educational interventions that prompt individuals to imagine living in an evacuation refuge affect their attentional responses to the refuge. Specifically, this study involved two conditions : one in which participants imagined living in a refuge using a tablet, and another in which participants imagined living in a refuge while conversing with others. Before and after these interventions, participants were shown images of the refuge, and their P3a and P3b event-related potentials (ERPs) were measured. The results showed that the P3a amplitude was significantly larger “after the intervention” compared to “before the intervention.” This suggests that the intervention made the refuge images more salient stimuli for the participants, leading to a greater allocation of attentional resources. Additionally, to investigate individual differences in attentional responses to the refuge, this study examined the correlation between P3a/P3b and empathy traits. The results indicated that individuals with higher cognitive empathy exhibited larger P3a amplitudes when imagining living in a refuge while conversing with others. This implies that for individuals with high empathy, the educational intervention of imagining living in a refuge through conversation with others serves as a significant trigger for attentional focus on the refuge.

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  In recent years, “real-world data (RWD)” has attracted much attention in the medical science. These data are expected to be used in a wide range of applications, such as personalized medicine and precision medicine. In the midst of this trend, the development of statistical methods for applying RWD is also progressing rapidly. The interest is in statistical inference on the effect of treatment on patients with arbitrary background information (covariates) (i.e., the difference between the outcome of the active and the standard regimen), i.e., the heterogeneous treatment effect (HTE). The statistical model for estimating HTE is the treatment effect model. Treatment effect models are being actively developed in the fields of statistical science and machine learning.
  In this paper, we organized treatment effect models based on a typology of subgroup identification methods, and evaluated their performance through numerical simulation and case studies. In addition, variable importance and partial dependence were examined as graphical representations for the treatment effect model.

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