Simulation study evaluates quantitative estimation of proportional hazards from Kaplan-Meier curves, suggesting improved assessment of cardiorenal survival data reliability.
Cox proportional hazard model (Cox) with a binary covariate is used widely to assess the effect of intervention (I) on cardiorenal event risks. As Kaplan-Meier (KM) curves of I and control (C) approximate proportional hazards (PH), quality of data analyzed using Cox becomes high. Quantitative methods for assessing PH are unknown. If PH can be quantitatively estimated from published KM curves of I and C, data reliability is easier to assess. Thus, we conducted a simulation study using real-world data for quantitative evaluation of PH.
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Takeishi et al. (2024) studied this question.
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