adjustedCurves

An R-Package to estimate and plot confounder-adjusted survival curves (single event survival data) and confounder-adjusted cumulative incidence functions (data with competing risks) using various methods.

https://github.com/robindenz1/adjustedcurves

Science Score: 49.0%

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Found 1 DOI reference(s) in README
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Keywords

adjusted confidence-intervals cumulative-incidence survival-curves

Last synced: 7 months ago · JSON representation

Repository

Basic Info

Host: GitHub
Owner: RobinDenz1
License: gpl-3.0
Language: R
Default Branch: main
Homepage: https://robindenz1.github.io/adjustedCurves/
Size: 30.5 MB

Statistics

Stars: 39
Watchers: 1
Forks: 6
Open Issues: 6
Releases: 7

Topics

adjusted confidence-intervals cumulative-incidence survival-curves

Created about 5 years ago · Last pushed 9 months ago

Metadata Files

Readme Changelog License Codemeta

README.Rmd

---
output: github_document
---



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# adjustedCurves 

Author: Robin Denz

## Description

`adjustedCurves` is an R-Package which can be used to estimate and plot confounder-adjusted survival curves + confidence intervals as well as cause-specific confounder-adjusted cumulative incidence functions + confidence intervals using a variety of methods. It provides a convenient wrapper around existing R-Packages on the topic and adds additional methods and functionality on top of it. Those additional features include the calculation of adjusted restricted mean survival times and testing whether two confounder-adjusted survival curves are different in a given interval.

Detailed descriptions of each method can be found in the literature cited in the documentation. 

## Installation

A stable version can be installed directly from CRAN using:

```{r, eval=FALSE}
install.packages("adjustedCurves")
```

The developmental version can be installed using the `devtools` R-Package:

```{r, eval=FALSE}
library(devtools)

install_github("https://github.com/RobinDenz1/adjustedCurves")
```

## Bug Reports and Feature Requests

If you encounter any bugs or have any specific feature requests, please file an [Issue](https://github.com/RobinDenz1/adjustedCurves/issues).

## Examples

This minimal example shows how to estimate and plot adjusted survival curves using *Direct Adjustment* with this package:

```{r}
library(adjustedCurves)
library(ggplot2)
library(survival)

# simulate some data as example
set.seed(31)
sim_dat <- sim_confounded_surv(n=250, max_t=1.2, group_beta=0)
sim_dat$group <- as.factor(sim_dat$group)

# estimate a cox-regression for the outcome
cox_mod <- coxph(Surv(time, event) ~ x1 + x2 + x4 + x5 + group,
                 data=sim_dat, x=TRUE)

# use it to estimate adjusted survival curves
adjsurv <- adjustedsurv(data=sim_dat,
                        variable="group",
                        ev_time="time",
                        event="event",
                        method="direct",
                        outcome_model=cox_mod,
                        conf_int=TRUE)

# plot with confidence intervals
plot(adjsurv, conf_int=TRUE)
```

Here is an example of how to estimate and plot adjusted survival curves using *Inverse Probability of Treatment Weighting*:

```{r}
# estimate a treatment assignment model
glm_mod <- glm(group ~ x2 + x3 + x5 + x6, data=sim_dat,
               family="binomial"(link="logit"))

# use it to estimate adjusted survival curves
adjsurv <- adjustedsurv(data=sim_dat,
                        variable="group",
                        ev_time="time",
                        event="event",
                        method="iptw_km",
                        treatment_model=glm_mod,
                        conf_int=TRUE)

# plot with confidence intervals
plot(adjsurv, conf_int=TRUE)
```

It is also possible to plot the difference between two curves using the `plot_curve_diff()` function:

```{r}
plot_curve_diff(adjsurv, conf_int=TRUE, color="blue")
```

To test whether the two adjusted survival curves are different in a specified interval (here 0 to 0.75), the `adjustedsurv` call has to be made with `bootstrap=TRUE`:

```{r}
adjsurv <- adjustedsurv(data=sim_dat,
                        variable="group",
                        ev_time="time",
                        event="event",
                        method="direct",
                        outcome_model=cox_mod,
                        conf_int=TRUE,
                        bootstrap=TRUE,
                        n_boot=1000)

adj_test <- adjusted_curve_test(adjsurv, from=0, to=0.75)
summary(adj_test)
```

More examples can be found in the documentation and the vignettes.

## Citation

The main paper associated with this R-Package is:

Robin Denz, Renate Klaaßen-Mielke, and Nina Timmesfeld (2023). *A comparison of different methods to adjust survival curves for confounders*. Statistics in Medicine. 42.10, pp. 1461-1479. 

In addition, the relevant primary literature of the respective method should be cited. This can be found in the documentation of the method.

## License

© 2024 Robin Denz

The contents of this repository are distributed under the GNU General Public License. You can find the full text of this License in this github repository. Alternatively, see .

Owner

Name: Robin Denz
Login: RobinDenz1
Kind: user

Repositories: 2
Profile: https://github.com/RobinDenz1

I am a researcher at the Ruhr-University of Bochum in Germany and am currently enrolled as a PhD Student in "Epidemiology & Clinical Research".

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Total packages: 1
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Total dependent packages: 0
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Total versions: 8
Total maintainers: 1

cran.r-project.org: adjustedCurves

Confounder-Adjusted Survival Curves and Cumulative Incidence Functions

Homepage: https://github.com/RobinDenz1/adjustedCurves
Documentation: http://cran.r-project.org/web/packages/adjustedCurves/adjustedCurves.pdf
License: GPL (≥ 3)
Latest release: 0.11.3
published 9 months ago

Versions: 8
Dependent Packages: 0
Dependent Repositories: 0
Downloads: 873 Last month

Rankings

Stargazers count: 15.1%

Downloads: 21.7%

Average: 26.2%

Forks count: 28.8%

Dependent packages count: 29.8%

Dependent repos count: 35.5%

Maintainers (1)

robin.denz@rub.de

Last synced: 8 months ago

Dependencies

DESCRIPTION cran

R.utils * imports
doParallel * imports
doRNG * imports
dplyr >= 1.0.0 imports
foreach * imports
rlang * imports
MASS * suggests
Matching >= 4.9 suggests
R6 * suggests
SuperLearner >= 2.0.0 suggests
WeightIt >= 0.11.0 suggests
cmprsk >= 2.2 suggests
covr * suggests
eventglm >= 1.1.1 suggests
geepack >= 1.3 suggests
ggplot2 >= 3.0.0 suggests
ggpp >= 0.4.3 suggests
knitr * suggests
mice >= 3.0.0 suggests
nnet * suggests
pammtools >= 0.5 suggests
pec >= 2020.11.17 suggests
prodlim >= 2019.11.13 suggests
riskRegression >= 2020.12.08 suggests
rmarkdown * suggests
survival >= 3.0.0 suggests
survtmle >= 1.1 suggests
testthat >= 3.0.0 suggests
tidyr * suggests
vdiffr >= 1.0.0 suggests

.github/workflows/check-standard.yaml actions

actions/checkout v3 composite
r-lib/actions/check-r-package v2 composite
r-lib/actions/setup-pandoc v2 composite
r-lib/actions/setup-r v2 composite
r-lib/actions/setup-r-dependencies v2 composite

.github/workflows/pkgdown.yaml actions

JamesIves/github-pages-deploy-action v4.4.1 composite
actions/checkout v3 composite
r-lib/actions/setup-pandoc v2 composite
r-lib/actions/setup-r v2 composite
r-lib/actions/setup-r-dependencies v2 composite

.github/workflows/test-coverage.yaml actions

actions/checkout v3 composite
r-lib/actions/setup-r v2 composite
r-lib/actions/setup-r-dependencies v2 composite

ecosyste.ms

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adjustedCurves

Science Score: 49.0%

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