GSAMU

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:

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:

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:

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.