https://github.com/seungjae2525/gsamu
GSAMU: Sensitivity analysis for effects of multiple exposures in the presence of unmeasured confounding: non-Gaussian and time-to-event outcomes
https://github.com/seungjae2525/gsamu
multiple-exposures non-gaussian r sensitivity-analysis time-to-event unmeasured-confounding
Last synced: 29 days ago
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GSAMU: Sensitivity analysis for effects of multiple exposures in the presence of unmeasured confounding: non-Gaussian and time-to-event outcomes
- Host: GitHub
- URL: https://github.com/seungjae2525/gsamu
- Owner: seungjae2525
- License: other
- Created: 2024-03-15T08:04:02.000Z (over 2 years ago)
- Default Branch: master
- Last Pushed: 2024-08-08T06:46:49.000Z (almost 2 years ago)
- Last Synced: 2024-08-08T10:29:00.109Z (almost 2 years ago)
- Topics: multiple-exposures, non-gaussian, r, sensitivity-analysis, time-to-event, unmeasured-confounding
- Language: R
- Homepage:
- Size: 116 KB
- Stars: 1
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.Rmd
- License: LICENSE
Awesome Lists containing this project
README
---
output: github_document
---
```{r, include = FALSE}
knitr::opts_chunk$set(
collapse = TRUE,
comment = "#>",
fig.path = "man/figures/README-",
out.width = "100%"
)
```
# GSAMU
[](https://www.repostatus.org/#active/)
[](https://github.com/seungjae2525/GSAMU/)
[](https://cran.r-project.org/)
[](https://lifecycle.r-lib.org/articles/stages.html#stable)
[](https://github.com/seungjae2525/GSAMU/actions/workflows/R-CMD-check.yaml)
## Description
This is the source code for the `GSAMU` package in R.
`GSAMU` is a package aimed at providing a novel sensitivity model to investigate the effect of correlated multiple exposures on the Gaussian, non-Gaussian, and time-to-event outcome variables.
Given a user-specified sensitivity parameters, the sensitivity interval is calculated. See reference for details.
## Reference
Lee S, Jeong B, Lee D, Lee W (2024): Sensitivity analysis for effects of multiple exposures in the presence of unmeasured confounding: non-Gaussian and time-to-event outcomes. submitted.
## Installation
### Current GitHub release:
Installation using R package `remotes`:
```r
if (!require("remotes", quietly=TRUE)) install.packages("remotes") # if remotes not already installed
remotes::install_github("seungjae2525/GSAMU")
library(GSAMU)
```
## Example
This is a basic example which shows you how to solve a common problem:
```{r example, eval=FALSE}
library(GSAMU)
### basic example code
## Set the bound of correlations
bound <- c(## Lower bound
# L1 L2 L3
0.01, 0.0, 0.0,
# X1, X2
rep(-0.19, 2),
# X3, X4
rep(-0.19, 2),
## Upper bound
# L1 L2 L3
0.32, 0.5, 0.29,
# X1, X2
rep(0.15, 2),
# X3, X4
rep(0.10, 2))
## For count outcome
continuous.re <- GSAMU(data=dataset,
outcome="Y", outcome.type="continuous",
link="identity", hazard.model=NULL,
confounder=c("L1", "L2", "L3"),
exposure=c("X1", "X2", "X3", "X4"),
delta=c(0.11, 0.22, 0.33, 0.44), bound=bound,
bootsCI=FALSE, B=1000, seed=231111, verbose=TRUE)
print(continuous.re)
## For count outcome
count.re <- GSAMU(data=dataset,
outcome="Y", outcome.type="count",
link="log", hazard.model=NULL,
confounder=c("L1", "L2", "L3"),
exposure=c("X1", "X2", "X3", "X4"),
delta=c(0.11, 0.22, 0.33, 0.44), bound=bound,
bootsCI=FALSE, B=1000, seed=231111, verbose=TRUE)
print(count.re)
## For binary outcome with logit link
binary.re1 <- GSAMU(data=dataset,
outcome="Y", outcome.type="count",
link="log", hazard.model=NULL,
confounder=c("L1", "L2", "L3"),
exposure=c("X1", "X2", "X3", "X4"),
delta=c(0.11, 0.22, 0.33, 0.44), bound=bound,
bootsCI=FALSE, B=1000, seed=231111, verbose=TRUE)
print(binary.re1)
## For binary outcome with logit link
binary.re2 <- GSAMU(data=dataset,
outcome="Y", outcome.type="count",
link="log", hazard.model=NULL,
confounder=c("L1", "L2", "L3"),
exposure=c("X1", "X2", "X3", "X4"),
delta=c(0.11, 0.22, 0.33, 0.44), bound=bound,
bootsCI=FALSE, B=1000, seed=231111, verbose=TRUE)
print(binary.re2)
## For time-to-event outcome with the cox PH model
cox.re <- GSAMU(data=dataset,
outcome=c("time", "status"), outcome.type="timetoevent",
link=NULL, hazard.model="coxph",
confounder=c("L1", "L2", "L3"),
exposure=c("X1", "X2", "X3", "X4"),
delta=c(0.11, 0.22, 0.33, 0.44), bound=bound,
bootsCI=FALSE, B=1000, seed=231111, verbose=TRUE)
print(cox.re)
## For time-to-event outcome with the additive hazard model
ah.re <- GSAMU(data=dataset,
outcome=c("time", "status"), outcome.type="timetoevent",
link=NULL, hazard.model="ah",
confounder=c("L1", "L2", "L3"),
exposure=c("X1", "X2", "X3", "X4"),
delta=c(0.11, 0.22, 0.33, 0.44), bound=bound,
bootsCI=FALSE, B=1000, seed=231111, verbose=TRUE)
print(ah.re)
```
You can also resulted plots, for example:
```{r pressure, eval=FALSE}
autoplot(object=continuous.re, point.size=2.75, width.SI=1.55, width.CI=0.6,
axis.title.x.size=15, axis.text.size=16, legend.text.size=15,
myxlim=c(-0.25, 2))
autoplot(object=count.re, point.size=2.75, width.SI=1.55, width.CI=0.6,
axis.title.x.size=15, axis.text.size=16, legend.text.size=15,
myxlim=c(-0.25, 2))
autoplot(object=binary.re1, point.size=2.75, width.SI=1.55, width.CI=0.6,
axis.title.x.size=15, axis.text.size=16, legend.text.size=15,
myxlim=c(-0.25, 2))
autoplot(object=binary.re2, point.size=2.75, width.SI=1.55, width.CI=0.6,
axis.title.x.size=15, axis.text.size=16, legend.text.size=15,
myxlim=c(-0.25, 2))
autoplot(object=cox.re, point.size=2.75, width.SI=1.55, width.CI=0.6,
axis.title.x.size=15, axis.text.size=16, legend.text.size=15,
myxlim=c(-0.25, 2))
autoplot(object=ah.re, point.size=2.75, width.SI=1.55, width.CI=0.6,
axis.title.x.size=15, axis.text.size=16, legend.text.size=15,
myxlim=c(-0.25, 2))
```
## Bug Reports:
You can also report bugs on GitHub under [Issues](https://github.com/seungjae2525/GSAMU/issues/).