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In many population-based medical studies, the specific cause of death is unidentified, unreliable or even unavailable. Relative survival analysis addresses this scenario, outside of standard (competing risks) survival analysis, to nevertheless estimate survival with respect to a specific cause. It separates the impact of the disease itself on mortality from other factors, such as age, sex, and general population trends. Different methods were created with the aim to construct consistent and efficient estimators for this purpose. The R package relsurv is the most commonly used today in application. With Julia continuously proving itself to be an efficient and powerful programming language, we felt the need to code a pure Julia take, thus NetSurvival.jl, of the standard routines and estimators in the field. The proposed implementation is clean, future-proof, well tested, and the package is correctly documented inside the rising JuliaSurv GitHub organization, ensuring trustability of the results. Through a comprehensive comparison in terms of performance and interface to relsurv, we highlight the benefits of the Julia developing environment.
Alhajal et al. (Wed,) studied this question.
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