diff --git a/dev/.documenter-siteinfo.json b/dev/.documenter-siteinfo.json index c8eaa3d..bb76a66 100644 --- a/dev/.documenter-siteinfo.json +++ b/dev/.documenter-siteinfo.json @@ -1 +1 @@ -{"documenter":{"julia_version":"1.10.2","generation_timestamp":"2024-04-12T12:15:00","documenter_version":"1.3.0"}} \ No newline at end of file +{"documenter":{"julia_version":"1.10.2","generation_timestamp":"2024-04-12T12:19:44","documenter_version":"1.3.0"}} \ No newline at end of file diff --git a/dev/examples/index.html b/dev/examples/index.html index b5ec127..8c79aac 100644 --- a/dev/examples/index.html +++ b/dev/examples/index.html @@ -1,5 +1,5 @@ -Examples · NetSurvival.jl
GitHub

Examples

In this section, we will be showcasing an example on how the key functions in the package work (with a comparaison to the results obtained in the relsurv package in R).

In this example, we will be using the dataset colrec which refers to patients with colon and rectal cancer diagnosed in 1994-2000. By loading the slopop rate table based on the Slovenian population, we will be able to apply the Pohar Perme estimator as well as the Grafféo log-rank test for net survival analysis purposes.

using NetSurvival, RateTables
+Examples · NetSurvival.jl
GitHub
Examples
In this section, we will be showcasing an example on how the key functions in the package work (with a comparaison to the results obtained in the relsurv package in R). 
In this example, we will be using the dataset colrec which refers to patients with colon and rectal cancer diagnosed in 1994-2000. By loading the slopop rate table based on the Slovenian population, we will be able to apply the Pohar Perme estimator as well as the Grafféo log-rank test for net survival analysis purposes.
using NetSurvival, RateTables
 pp1 = fit(PoharPerme, @formula(Surv(time,status)~1), colrec, slopop)
8149×5 DataFrame
   Row │ Sₑ          ∂Λₑ            σₑ             lower_95_CI  upper_95_CI 
       │ Float64     Float64        Float64        Float64      Float64     
@@ -100,4 +100,4 @@
 Value of test statistic: 658.6024 
 Degrees of freedom: 3 
 P value: 0 
-

+
diff --git a/dev/getting_started/index.html b/dev/getting_started/index.html index cdf78bf..ca00d02 100644 --- a/dev/getting_started/index.html +++ b/dev/getting_started/index.html @@ -1,2 +1,2 @@ -Getting Started · NetSurvival.jl
GitHub

Getting Started

Pohar Perme

The Pohar Perme[1] is a statistical method used in survival analysis to estimate net survival probabilities, particularly designed to handle situations where covariates may change over time. The net survival function is defined as:

\[S_{E}(t) = exp(-\int_0^t\lambda_{E}(u)du)\]

The $\lambda_E$ is the associated hazard given by :

\[\lambda_E (t) = \frac{\sum_{i=1}^{N}S_{E_i}(t)\lambda_{E_i}(t)}{\sum_{i=1}^{N}S_{E_i}(t)}\]

This weighted average is thus based on the likelihood that an individual remains alive in a hypothetical scenario where the disease is the sole cause of death.

Ederer II

Grafféo Log-Rank Test

The Grafféo Log-Rank Test [2] was constructed as a complement to the Pohar Perme estimator, aiming to compare the net survival functions provided by the latter. The test is designed to compare these functions across multiple groups, including stratified covariables, and to ultimately determine, with the given p-value, which covariables are impactful to the study.

For this test, we first define the number of deaths caused by the event studied for a time $s$ within the group $h$ noted $N_{E,h}(s)$ and the process of individuals at risk within the same group $h$ at time $s$ noted $Y_h(s)$. Both of these values are weighted with the populational estimated survival for the given patient, same as in Pohar Perme.

The $(H_0)$ hypothesis tested

[1]
M. P. Perme, J. Stare and J. Estève. On Estimation in Relative Survival. Biometrics 68, 113–120 (2011).
[2]
N. Grafféo, F. Castell, A. Belot and R. Giorgi. A Log-Rank-Type Test to Compare Net Survival Distributions. Biometrics 72, 760–769 (2016).
+Getting Started · NetSurvival.jl
GitHub

Getting Started

Pohar Perme

The Pohar Perme[1] is a statistical method used in survival analysis to estimate net survival probabilities, particularly designed to handle situations where covariates may change over time. The net survival function is defined as:

\[S_{E}(t) = exp(-\int_0^t\lambda_{E}(u)du)\]

The $\lambda_E$ is the associated hazard given by :

\[\lambda_E (t) = \frac{\sum_{i=1}^{N}S_{E_i}(t)\lambda_{E_i}(t)}{\sum_{i=1}^{N}S_{E_i}(t)}\]

This weighted average is thus based on the likelihood that an individual remains alive in a hypothetical scenario where the disease is the sole cause of death.

Ederer II

Grafféo Log-Rank Test

The Grafféo Log-Rank Test [2] was constructed as a complement to the Pohar Perme estimator, aiming to compare the net survival functions provided by the latter. The test is designed to compare these functions across multiple groups, including stratified covariables, and to ultimately determine, with the given p-value, which covariables are impactful to the study.

For this test, we first define the number of deaths caused by the event studied for a time $s$ within the group $h$ noted $N_{E,h}(s)$ and the process of individuals at risk within the same group $h$ at time $s$ noted $Y_h(s)$. Both of these values are weighted with the populational estimated survival for the given patient, same as in Pohar Perme.

The $(H_0)$ hypothesis tested

[1]
M. P. Perme, J. Stare and J. Estève. On Estimation in Relative Survival. Biometrics 68, 113–120 (2011).
[2]
N. Grafféo, F. Castell, A. Belot and R. Giorgi. A Log-Rank-Type Test to Compare Net Survival Distributions. Biometrics 72, 760–769 (2016).
diff --git a/dev/index.html b/dev/index.html index 59da362..b25c179 100644 --- a/dev/index.html +++ b/dev/index.html @@ -1,2 +1,2 @@ -Home · NetSurvival.jl
GitHub

Introduction

This package serves to provide the necessary tools to perform net survival analysis, a branch of survival analysis dedicated to estimating the probability of survival from a particular event of interest compared to the general public. Some key features in NetSurvival.jl are:

  • Fitting different non-parametric estimators (Pohar Perme[1], Ederer II, ...)
  • Applying Grafféo's log-rank test[2] on different groups, including stratified covariables
  • ...

Installation

The package is available on Julia's general registry, and can be installed either with the command Pkg.add("NetSurvival") or via the Pkg REPL mode: 

] add NetSurvival
NetSurvival.PoharPerme — Type
PoharPerme

This method was newly introduced in 2012 and it has been globally recognized since as the more effective method out of the net survival estimators. To call this function : 

fit(PoharPerme, @formula(Surv(time,status)~covariable1 + covariable2), data, ratetable)
source
[1]
M. P. Perme, J. Stare and J. Estève. On Estimation in Relative Survival. Biometrics 68, 113–120 (2011).
[2]
N. Grafféo, F. Castell, A. Belot and R. Giorgi. A Log-Rank-Type Test to Compare Net Survival Distributions. Biometrics 72, 760–769 (2016).
+Home · NetSurvival.jl
GitHub

Introduction

This package serves to provide the necessary tools to perform net survival analysis, a branch of survival analysis dedicated to estimating the probability of survival from a particular event of interest compared to the general public. Some key features in NetSurvival.jl are:

  • Fitting different non-parametric estimators (Pohar Perme[1], Ederer II, ...)
  • Applying Grafféo's log-rank test[2] on different groups, including stratified covariables
  • ...

Installation

The package is available on Julia's general registry, and can be installed either with the command Pkg.add("NetSurvival") or via the Pkg REPL mode: 

] add NetSurvival
NetSurvival.PoharPerme — Type
PoharPerme

This method was newly introduced in 2012 and it has been globally recognized since as the more effective method out of the net survival estimators. To call this function : 

fit(PoharPerme, @formula(Surv(time,status)~covariable1 + covariable2), data, ratetable)
source
[1]
M. P. Perme, J. Stare and J. Estève. On Estimation in Relative Survival. Biometrics 68, 113–120 (2011).
[2]
N. Grafféo, F. Castell, A. Belot and R. Giorgi. A Log-Rank-Type Test to Compare Net Survival Distributions. Biometrics 72, 760–769 (2016).
diff --git a/dev/objects.inv b/dev/objects.inv index 5a26efc..583a758 100644 --- a/dev/objects.inv +++ b/dev/objects.inv @@ -2,8 +2,5 @@ # Project: NetSurvival.jl # Version: 1.0.0-DEV # The remainder of this file is compressed using zlib. -xAn0E>HEUWeWU, -%Wnnv6S# ]g/Wv[-G4y~<=>-$H =B`\T+Pɛ}wC[Hec5#^M|Atޅ$1C|ƴ \ No newline at end of file +xMN0>Hm&BXBJ#OBɴ*G\Xݍ|ovвY_x +kWLh G,SӠ% ,sOM ga_Fb R!]_^>Jf}glyK.iu=dֿi[/^B`!"5F$HPJ(;ڻ-4VV(ɳBt^&]wB%ԃ \ No newline at end of file diff --git a/dev/references/index.html b/dev/references/index.html index 1ee31d4..2832efe 100644 --- a/dev/references/index.html +++ b/dev/references/index.html @@ -1,2 +1,2 @@ -References · NetSurvival.jl
GitHub
+References · NetSurvival.jl
GitHub