https://github.com/nanxstats/hdnom
🔮 Benchmarking and visualization toolkit for penalized Cox models
https://github.com/nanxstats/hdnom
benchmark high-dimensional-data linear-regression nomogram-visualization penalized-cox-models survival-analysis
Last synced: 13 days ago
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🔮 Benchmarking and visualization toolkit for penalized Cox models
- Host: GitHub
- URL: https://github.com/nanxstats/hdnom
- Owner: nanxstats
- License: gpl-3.0
- Created: 2015-08-04T18:05:55.000Z (almost 10 years ago)
- Default Branch: master
- Last Pushed: 2024-09-05T18:06:40.000Z (9 months ago)
- Last Synced: 2025-05-06T07:00:29.101Z (14 days ago)
- Topics: benchmark, high-dimensional-data, linear-regression, nomogram-visualization, penalized-cox-models, survival-analysis
- Language: R
- Homepage: https://nanx.me/hdnom/
- Size: 28 MB
- Stars: 43
- Watchers: 7
- Forks: 11
- Open Issues: 1
-
Metadata Files:
- Readme: README.Rmd
- Changelog: NEWS.md
- Contributing: CONTRIBUTING.md
- License: LICENSE
- Code of conduct: CODE_OF_CONDUCT.md
Awesome Lists containing this project
README
---
output: github_document
---
```{r, include=FALSE}
knitr::knit_hooks$set(pngquant = knitr::hook_pngquant)
knitr::opts_chunk$set(
collapse = TRUE,
comment = "#>",
fig.path = "man/figures/README-",
out.width = "100%",
echo = FALSE,
message = FALSE,
warning = FALSE,
dev = "ragg_png",
dpi = 72,
fig.retina = 2,
fig.align = "center",
out.width = "100%",
pngquant = "--speed=1 --quality=50"
)
```
# hdnom 
[](https://github.com/nanxstats/hdnom/actions/workflows/R-CMD-check.yaml)
[](https://cran.r-project.org/package=hdnom)
[](https://cranlogs.r-pkg.org/badges/hdnom)
`hdnom` creates nomogram visualizations for penalized Cox regression models, with the support of reproducible survival model building, validation, calibration, and comparison for high-dimensional data.
## Installation
You can install `hdnom` from CRAN:
```r
install.packages("hdnom")
```
Or try the development version on GitHub:
```r
remotes::install_github("nanxstats/hdnom")
```
Browse [the vignettes](https://nanx.me/hdnom/articles/) to get started.
## Gallery
### Nomogram
```{r, nomogram, fig.width=10, fig.height=6}
library("hdnom")
library("survival")
library("gridExtra")
library("cowplot")
library("ggplot2")
data(smart)
x <- as.matrix(smart[, -c(1, 2)])
time <- smart$TEVENT
event <- smart$EVENT
y <- Surv(time, event)
# Fit penalized Cox model with lasso penalty
lassofit <- fit_lasso(x, y, nfolds = 10, rule = "lambda.1se", seed = 11)
# Plot nomogram
nom <- as_nomogram(
lassofit, x, time, event,
pred.at = 365 * 2,
funlabel = "2-Year Overall Survival Probability"
)
plot(nom)
```
### Kaplan-Meier plot with number at risk table
```{r, kmplot, fig.width=10, fig.height=6}
# Internal calibration
cal.int <- calibrate(
x, time, event,
model.type = "lasso",
alpha = 1, lambda = lassofit$"lambda",
method = "repeated.cv", nfolds = 10, rep.times = 20,
pred.at = 365 * 9, ngroup = 3,
trace = FALSE
)
kmplot(
cal.int,
group.name = c("High risk", "Medium risk", "Low risk"),
time.at = 1:8 * 365
)
```
### Model validation and calibration
```{r, model-validation-calibration, fig.width=15, fig.height=6}
x <- as.matrix(smart[, -c(1, 2)])[1:500, ]
time <- smart$TEVENT[1:500]
event <- smart$EVENT[1:500]
y <- Surv(time, event)
# Fit penalized Cox model
lassofit <- fit_lasso(x, y, nfolds = 5, rule = "lambda.1se", seed = 11)
# Model validation by repeated cross-validation with time-dependent AUC
val.repcv <- validate(
x, time, event,
model.type = "lasso",
alpha = 1, lambda = lassofit$"lambda",
method = "repeated.cv", nfolds = 5, rep.times = 20,
tauc.type = "UNO", tauc.time = seq(0.5, 2, 0.25) * 365,
seed = 1010,
trace = FALSE
)
# Model calibration by repeated cross-validation
cal.repcv <- calibrate(
x, time, event,
model.type = "lasso",
alpha = 1, lambda = lassofit$"lambda",
method = "repeated.cv", nfolds = 10, rep.times = 20,
pred.at = 365 * 9, ngroup = 5,
seed = 1010,
trace = FALSE
)
invisible(capture.output(
p1 <- plot(val.repcv, ylim = c(0.3, 0.9)) +
theme_cowplot()
))
invisible(capture.output(
p2 <- plot(cal.repcv) +
theme_cowplot()
))
grid.arrange(p1, p2, ncol = 2)
```
### Model comparison by validation or calibration
```{r, model-comparison, fig.width=15, fig.height=6}
# Compare lasso and adaptive lasso by 5-fold cross-validation
cmp.val.cv <- compare_by_validate(
x, time, event,
model.type = c("lasso", "alasso"),
method = "repeated.cv", nfolds = 10, rep.times = 20,
tauc.type = "UNO",
tauc.time = seq(1, 4, 0.5) * 365, seed = 1001,
trace = FALSE
)
# Compare lasso and adaptive lasso by 5-fold cross-validation
cmp.cal.cv <- compare_by_calibrate(
x, time, event,
model.type = c("lasso", "alasso"),
method = "fitting",
pred.at = 365 * 9, ngroup = 5, seed = 1001,
trace = FALSE
)
invisible(capture.output(
p3 <- plot(cmp.val.cv, interval = TRUE) +
theme_cowplot() +
theme(legend.position = "bottom")
))
invisible(capture.output(
p4 <- plot(cmp.cal.cv) +
theme_cowplot() +
theme(legend.position = "bottom")
))
grid.arrange(p3, p4, ncol = 2)
```
## Shiny app
- Shiny app:
- Shiny app maker:
## Contribute
To contribute to this project, please take a look at the
[Contributing Guidelines](https://nanx.me/hdnom/CONTRIBUTING.html) first.
Please note that the hdnom project is released with a
[Contributor Code of Conduct](https://nanx.me/hdnom/CODE_OF_CONDUCT.html).
By contributing to this project, you agree to abide by its terms.