擬似値（Pseudo observations）を用いた生存時間解析

抄録

In survival analysis, modeling the hazard function, such as the Cox proportional hazards model, has been widely applied. However, there are some problems discussed in recent years. For example, the proportional hazards assumption is violated in some situations such as immunotherapies for cancer treatments. The other problem is the causal interpretability of hazard ratios discussed in the causal inference area. Therefore, as an alternative to modeling via hazard function, more flexible and interpretable methods for survival analysis are required. Pseudo-observations in survival analysis can allow for clinically interpretable analysis methods. It is applicable under a general framework on a generalized linear model if it is possible to compute pseudo-observations for the target of interest. In this paper, we explain the fundamental concept of pseudo-observations in survival analysis and the asymptotic theory for the estimator of regression coefficients while providing an overview of the related discussions.