survdistr

Survival distribution container for efficient storage, management, and interpolation of survival model predictions.

Installation

Install the development version from GitHub:

# install.packages("pak")
pak::pak("mlr-org/survdistr")

Examples

Linear interpolation of a survival matrix using the survDistr R6 class:

library(survdistr)

# generate survival matrix
mat = matrix(data = c(0.9,0.6,0.4,0.8,0.8,0.7), nrow = 2,
             ncol = 3, byrow = TRUE)
x = survDistr$new(x = mat, times = c(12, 34, 42), method = "linear_surv")
x

## A [2 x 3] survival matrix
## Number of observations: 2
## Number of time points: 3
## Interpolation method: Piecewise Constant Density (Linear Survival)

# stored survival matrix
x$data()

##       12  34  42
## [1,] 0.9 0.6 0.4
## [2,] 0.8 0.8 0.7

# S(t) at requested time points (linear interpolation)
x$survival(times = c(5, 30, 42, 50))

##              5        30  42  50
## [1,] 0.9583333 0.6545455 0.4 0.2
## [2,] 0.9166667 0.8000000 0.7 0.6

# Cumulative hazard H(t)
x$cumhazard(times = c(5, 42))

##               5        42
## [1,] 0.04255961 0.9162907
## [2,] 0.08701138 0.3566749

# Probability density f(t)
x$density(times = c(5, 30, 42))

##                5         30     42
## [1,] 0.008333333 0.01363636 0.0250
## [2,] 0.016666667 0.00000000 0.0125

# Hazard h(t)
x$hazard(times = c(5, 30, 42))

##                5         30         42
## [1,] 0.008695652 0.02083333 0.06250000
## [2,] 0.018181818 0.00000000 0.01785714

Interpolation of a Kaplan-Meier survival curve using exported R function that calls C++ code:

library(survival)

fit = survfit(formula = Surv(time, status) ~ 1, data = veteran)
tab = data.frame(time = fit$time, surv = fit$surv)
head(tab)

##   time      surv
## 1    1 0.9854015
## 2    2 0.9781022
## 3    3 0.9708029
## 4    4 0.9635036
## 5    7 0.9416058
## 6    8 0.9124088

tail(tab)

##     time        surv
## 96   411 0.045022553
## 97   467 0.036018043
## 98   553 0.027013532
## 99   587 0.018009021
## 100  991 0.009004511
## 101  999 0.000000000

# constant S(t) interpolation
interp(
  x = tab$surv,
  times = tab$time,
  eval_times = c(0, 3.5, 995)
)

##           0         3.5         995 
## 1.000000000 0.970802920 0.009004511

# linear S(t) interpolation
interp(
  x = tab$surv,
  times = tab$time,
  eval_times = c(0, 3.5, 995),
  method = "linear_surv"
)

##           0         3.5         995 
## 1.000000000 0.967153285 0.004502255

# exponential S(t) interpolation
interp(
  x = tab$surv,
  times = tab$time,
  eval_times = c(0, 3.5, 995),
  method = "exp_surv"
)

##         0       3.5       995 
## 1.0000000 0.9671464 0.0000000

Code of Conduct

Please note that the survdistr project is released with a Contributor Code of Conduct. By contributing to this project, you agree to abide by its terms.