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In radiological research, survival analysis has been increasingly used to evaluate prognostic outcomes Researchers may be familiar with the use of Cox proportional hazards (PH) regression to quantify the effect of predictors, such as treatment, imaging, or radiological variables, using hazard ratios. Cox regression requires the proportional hazards assumption, which means that the ratio of hazards between groups is constant over the entire study period, to be valid; however, this scenario is rarely achieved with realworld data. In addition, the hazard ratio simply quantifies the relative difference in risk based on a model-based approach; therefore, it is difficult to interpret the absolute effect directly. To overcome these limitations, other types of Cox regression, such as stratified Cox regression or Cox regression with time-varying covariates, or parametric survival models, such as the accelerated failure time model, can be applied; however, these analytical methods still yield hazard ratios as the output. Other traditional options for the output in survival analysis include several model-free summary measures based on survival rate at a given time (e.g., 1-year survival) or percentiles of the survival function (e.g., median survival time). Interestingly, a more simplified and intuitive approach, namely RMST, has been recently proposed as an alternative output in survival analysis to hazard ratio
Han et al. (Sat,) studied this question.