NetSurvival.jl

A pure-Julia take on standard net survival routines

The NetSurvival.jl package is part of the JuliaSurv survival analysis suite. It provides the necessary tools to perform estimations and analysis in the Net Survival field. This specialized branch of Survival Analysis focuses on estimating the probability of survival from a specific event of interest, for example a given cancer, without considering other causes of death, in the (unfortunately quite common) case where the cause of death indicatrix is unavailable (or e.g. untrustworthy). Consequently, the so-called missing indicatrix issue forbids the use of standard competitive risks survival analysis methods on these datasets. Thus, a few standard estimators were established in the last 50 years, backed by a wide literature.

Features

This package is an attempt to bring standard relative survival analysis modeling routines to Julia, while providing an interface that is close to the R package relsurv, albeit significantly faster and easier to maintain in the future. We aim at covering the standard estimators, needed for routines and comparisons, but also to provide the most up to date state of the art.

Some key features in NetSurvival.jl are:

• A panel of different non-parametric net survival estimators (Ederer I, Ederer II, Hakulinen, Pohar Perme) with an interface compliant with Julia's standards.
• Grafféo's log-rank test to compare net survival curves accross groups, including stratified testing.
• A 'Nessie' function that outputs the estimated sample size by yearly intervals and the average lifespan expectancy left for a given group.
• A compact, readable and efficient codebase (up to 100x less LOC than relsurv for the same functionalities), ensuring long-term maintenability.
• Significant performance improvements (see below) compared relsurv.

Getting Started

The package is available on Julia's general registry, and thus can be installed through the following command:

using NetSurvival

See the rest of this documentation to have a glimpse of the functionalities! You can also take a look at our talk at JuliaCon2024.

Benchmarks

NetSurvival.jl is fast. Below numbers gives runtime mulitpliers w.r.t. R::relsurv, computed on a i9-13900 processor. A version of these numbers computed on (even slower) github action's runners are availiable in our documentation, alongside the code needed to re-ran these numbers on your environnement.

The comparison is done on the colrec dataset with the slopop ratetable. The first numbers compare the timing in the obtention of the net survival curve:

	Unstratified Surv(time,status)~1	Stratified Surv(time,status)~sex
Pohar Perme	20.8431	20.1461
EdererI	7.216	4.1363
EdererII	29.2397	29.0399
Hakulinen	23.493	15.6676

While the second numbers compare the implementation the Grafféo's log-rank-type test:

	Unstratified Surv(time,status)~stage	Stratified Surv(time,status)~stage+Strata(sex)
Graffeo's LRT	13.1556	18.156

Call to contributions : If you have access to stata's implementation (which is not free) and want to report timings, do not hesitate to open an issue.

Contributions are welcome

If you want to contribute to the package, ask a question, found a bug or simply want to chat, do not hesitate to open an issue on this repo. General guidelines on collaborative practices (colprac) are available at https://github.com/SciML/ColPrac.