influenceTest(object, ...)

## S3 method for class 'list'
influenceTest(
  object,
  newdata,
  times,
  type,
  cause,
  keep.newdata = TRUE,
  keep.strata = FALSE,
  ...
)

## Default S3 method:
influenceTest(object, object2, band = TRUE, ...)

library(lava)
library(survival)
library(prodlim)
library(data.table)
n <- 100

#### Under H1
set.seed(1)
newdata <- data.frame(X1=0:1)

## simulate non proportional hazard using lava
m <- lvm()
regression(m) <- y ~ 1
regression(m) <- s ~ exp(-2*X1)
distribution(m,~X1) <- binomial.lvm()
distribution(m,~cens) <- coxWeibull.lvm(scale=1)
distribution(m,~y) <- coxWeibull.lvm(scale=1,shape=~s)
eventTime(m) <- eventtime ~ min(y=1,cens=0)
d <- as.data.table(sim(m,n))
setkey(d, eventtime)

## fit cox models
m.cox <- coxph(Surv(eventtime, status) ~ X1, 
               data = d, y = TRUE, x = TRUE)

mStrata.cox <- coxph(Surv(eventtime, status) ~ strata(X1), 
                     data = d, y = TRUE, x = TRUE)

## compare models
# one time point
outIF <- influenceTest(list(m.cox, mStrata.cox), 
              type = "survival", newdata = newdata, times = 0.5)
confint(outIF)
                                 
# several timepoints
outIF <- influenceTest(list(m.cox, mStrata.cox), 
              type = "survival", newdata = newdata, times = c(0.5,1,1.5))
confint(outIF)

#### Under H0 (Cox) ####
set.seed(1)
## simulate proportional hazard using lava
m <- lvm()
regression(m) <- y ~ 1
distribution(m,~X1) <- binomial.lvm()
distribution(m,~cens) <- coxWeibull.lvm()
distribution(m,~y) <- coxWeibull.lvm()
eventTime(m) <- eventtime ~ min(y=1,cens=0)
d <- as.data.table(sim(m,n))
setkey(d, eventtime)

## fit cox models
Utime <- sort(unique(d$eventtime))
m.cox <- coxph(Surv(eventtime, status) ~ X1, 
               data = d, y = TRUE, x = TRUE)

mStrata.cox <- coxph(Surv(eventtime, status) ~ strata(X1), 
                     data = d, y = TRUE, x = TRUE)

p.cox <- predictCox(m.cox, newdata = newdata, time = Utime, type = "survival")
p.coxStrata <- predictCox(mStrata.cox, newdata = newdata, time = Utime, type = "survival")

## display
library(ggplot2)
autoplot(p.cox)
autoplot(p.coxStrata)
 
## compare models
outIF <- influenceTest(list(m.cox, mStrata.cox), 
                       type = "survival", newdata = newdata, times = Utime[1:6])
confint(outIF)

#### Under H0 (CSC) ####
set.seed(1)
ff <- ~ f(X1,2) + f(X2,-0.033)
ff <- update(ff, ~ .+ f(X3,0) + f(X4,0) + f(X5,0))
ff <- update(ff, ~ .+ f(X6,0) + f(X7,0) + f(X8,0) + f(X9,0))
d <- sampleData(n, outcome = "competing.risk", formula = ff)
d[,X1:=as.numeric(as.character(X1))]
d[,X2:=as.numeric(as.character(X2))]
d[,X3:=as.numeric(as.character(X3))]
d[,X4:=as.numeric(as.character(X4))]
d[,X5:=as.numeric(as.character(X5))]
setkey(d, time)

Utime <- sort(unique(d$time))

## fit cox models
m.CSC <- CSC(Hist(time, event) ~ X1 + X2, data = d)
mStrata.CSC <- CSC(Hist(time, event) ~ strata(X1) + X2 + X3, data = d)

## compare models
outIF <- influenceTest(list(m.CSC, mStrata.CSC), 
             cause = 1, newdata = unique(d[,.(X1,X2,X3)]), times = Utime[1:5])
confint(outIF)