require(mediation)

input: 
df1<-  #data frame including participant information of interest
df2<-read.csv("models.csv", header=T, strings=F, sep=";") # Additional file 7 provides the file we used in our discovery

R <- nrow(df2)
B = matrix(nrow = R, ncol = 15,
           dimnames = list(paste('Model',1:R),
                           c("mediator","outcome", "ACME_est", "ACME_L", "ACME_U","ADE_est", "ADE_L", "ADE_U", "TE_est", "TE_L", "TE_U", "PM_est", "PM_L", "PM_U", "Rho_0")))

df3<-df1[complete.cases(df1), ]
str(df3)

dataFrame<- df2
for(i in 1:nrow(dataFrame)) {
  a <- dataFrame[[i,1]]
  b <- dataFrame[i,2]
  B[i,1] <-a
  B[i,2] <-b
  
  df3$med<-df3[[a]]
  df3$outcome<-df3[[b]]
  
  m1 <- lm(med ~ current_smoking + Age + sex, df3)
  y1 <- lm(outcome ~ med + current_smoking + Age + sex, df3)
  set.seed(2015)
  m.out1<-mediate(m1, y1, treat = "current_smoking", mediator= "med", boot= TRUE, sims = 1000)
  ys.out_SE <- medsens(m.out1)
  B[i,3] <- m.out1$d0
  g<-as.data.frame(m.out1$d0.ci)
  B[i,4]<- g[1,] ## lower
  B[i,5]<- g[2,] ## upper
  
  B[i,6]<- m.out1$z0
  h<-as.data.frame(m.out1$z0.ci)
  B[i,7]<- h[1,] ## lower
  B[i,8]<- h[2,] ## upper
  
  B[i,9]<- m.out1$tau.coef
  l<-as.data.frame(m.out1$tau.ci)
  B[i,10]<- l[1,] ## lower
  B[i,11]<- l[2,] ## upper
  
  B[i,12]<- m.out1$n.avg
  m<-as.data.frame(m.out1$n.avg.ci)
  B[i,13]<- m[1,] ## lower
  B[i,14]<- m[2,] ## upper
  B[i,15]<- ys.out_SE$err.cr.d

}

write.csv(B, "mediaton_models_results.csv", quote=F, row.names=F)