Umediation

The Umediation R package enables the user to simulate unmeasured confounding in mediation analysis in order to see how the results of the mediation analysis would change in the presence of unmeasured confounding.

Installation

install.packages("devtools") #The devtools package must be installed first
install.packages("mediation") #The mediation package must be installed first
install.packages("car") #The car package must be installed first

devtools::install_github("SharonLutz/Umediation")

Example

Below, we simulate 4 unmeasured confounders U (2 normally distributed and 2 Bernouilli distributed random variables) on the binary exposure, A, normally distributed mediator, M, and normally distributed outcome Y adjusted for one normally distributed covariate and 2 binary distributed covariates.

library(Umediation)
?Umediation # For details on this function and how to choose input variables

testM<- Umediation(n=1000,Atype="D",Mtype="C",Ytype="C",Ctype=c("C","D","D"),Utype=c("C","D","D","C"),interact=TRUE,muC=c(0.1,0.3,0.2),
varC=c(1,1,1),muU=c(.1,0.3,0.2,.1),varU=c(1,1,1,1),gamma0=0,gammaC=c(1,0.3,0.2),gammaU=c(1,0.3,0.2,0.4),varA=1,alpha0=0,alphaA=1,
alphaC=c(0.3,0.2,0.2),alphaU=c(0.3,0.2,0.3,0.2),varM=1,beta0=0,betaA=-1,betaM=1,betaI=1,betaC=c(0.3,0.2,0.1),betaU=c(0.3,0.2,-1.3,0.2),
varY=1,alpha=0.05,nSim=100,nBoot=400)

testM

Output

For this analysis, we can see that there is not a significant difference in the proportion of simulations for the mediated effect if the unmeasured confounders are included, but there is a large diffence in the inference for the direct effect if these unmeasured confounders are not included in the analysis.

$Results
                                                                               [,1]
Prop. of simulations w/ significant ACME excluding U                    1.000000000
Prop. of simulations w/ significant ACME including U                    1.000000000
Prop. of simulations where conclusions based on ACME match              1.000000000
Average ACME excluding U                                                2.061403587
Average ACME including U                                                1.494588641
Average absolute difference of ACME including U minus ACME excluding U  0.566814946
Prop. of simulations w/ significant ADE excluding U                     0.030000000
Prop. of simulations w/ significant ADE including U                     0.450000000
Prop. of simulations where conclusions based on ADE match               0.520000000
Average ADE excluding U                                                 0.006946501
Average ADE including U                                                -0.185434050
Average absolute difference of ADE including U minus ADE excluding U    0.192380551

$Correlations_Between_Variables
      A    M     Y    C1    C2    C3    U1    U2    U3    U4
A  1.00 0.56  0.49  0.31  0.04  0.07  0.37  0.05  0.03  0.16
M  0.56 1.00  0.87  0.33  0.08  0.09  0.35  0.11  0.15  0.26
Y  0.49 0.87  1.00  0.37  0.09  0.08  0.38  0.11 -0.08  0.27
C1 0.31 0.33  0.37  1.00  0.04  0.01 -0.03 -0.01  0.00 -0.01
C2 0.04 0.08  0.09  0.04  1.00  0.03 -0.03  0.02  0.00  0.02
C3 0.07 0.09  0.08  0.01  0.03  1.00  0.01  0.01  0.02 -0.01
U1 0.37 0.35  0.38 -0.03 -0.03  0.01  1.00 -0.01  0.00 -0.04
U2 0.05 0.11  0.11 -0.01  0.02  0.01 -0.01  1.00  0.01 -0.02
U3 0.03 0.15 -0.08  0.00  0.00  0.02  0.00  0.01  1.00  0.04
U4 0.16 0.26  0.27 -0.01  0.02 -0.01 -0.04 -0.02  0.04  1.00

$Warning
[1] "Warning: correlations are only valid if at least one of the variables is normally distributed."

Reference

Lutz SM, Thwing A, Schmiege S, Kroehl M, Baker CD, Starling AP, Hokanson JE, Ghosh D. (2017) Examining the Role of Unmeasured Confounding in Mediation Analysis with Genetic and Genomic Applications. BMC Bioinformatics. 18(1):344.