Survode

The goal of survode is to implement the method described in Tang et al. (2022) [1] for fitting survival models using ordinary differential equations. The package is currently under development and is not yet ready for use.

Installation

You can install the development version of survode like so:

# install.packages("devtools")
devtools::install_github("SignorinoY/survode")

Example

This is a basic example which shows you how to solve a common problem:

library(simsurv)
library(survode)
#> 载入需要的程辑包：survival

# Create a simple data set
set.seed(42)
cov <- data.frame(
  id = 1:400,
  trt = stats::rbinom(400, 1L, 0.5),
  hormon = stats::rnorm(400, 0, 1)
)
dat <- simsurv(
  lambdas = 0.1, gammas = 1.5, betas = c(trt = -0.5, hormon = 1.0),
  x = cov, maxt = 5
)
sim <- merge(cov, dat)

# Fit a Cox model with a spline for the baseline hazard
fit <- survode(Surv(eventtime, status) ~ trt + hormon, data = sim, df = 5)
fit$coefficients$beta
#>        trt     hormon 
#> -0.6693948  0.9394916

# Predict the baseline hazard at times (0, 5)
bh <- predict(fit, type = "hazard", time = seq(0, 5, length.out = 100))
par(mfrow = c(1, 2), cex = 0.85)
plot(
  bh$time, bh$basehaz,
  type = "l", xlab = "Time", ylab = "Baseline hazard"
)
lines(bh$time, 0.15 * bh$time^0.5, col = "red")
plot(
  bh$time, bh$cumhaz,
  type = "l", xlab = "Time", ylab = "Cumulative baseline hazard"
)
lines(bh$time, 0.1 * bh$time^1.5, col = "red")

Code of Conduct

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

[1] Tang, W. et al. (2022) ‘Survival analysis via ordinary differential equations’, Journal of the American Statistical Association, pp. 1–16. Available at: https://doi.org/10.1080/01621459.2022.2051519.