# R/sampleData.R In riskRegression: Risk Regression Models and Prediction Scores for Survival Analysis with Competing Risks

#### Documented in sampleDatasampleDataTD

```### sampleData.R ---
#----------------------------------------------------------------------
## author: Thomas Alexander Gerds
## created: Jan  4 2016 (09:43)
## Version:
## last-updated: Mar  9 2022 (15:45)
##           By: Thomas Alexander Gerds
##     Update #: 53
#----------------------------------------------------------------------
##
### Commentary:
##
### Change Log:
#----------------------------------------------------------------------
##
### Code:
##' Simulate data with binary outcome and 10 covariates.
##'
##' For the actual lava::regression parameters see the function definition.
##' @title Simulate data with binary or time-to-event outcome
##' @aliases sampleDataTD
##' @param n Sample size
##' @param n.intervals \code{sampleDataTD} only: the maximum number of episodes in which the covariates are updated.
##' @param outcome Character vector. Response variables are generated
##' according to keywords: \code{"binary"} = binary response,
##' \code{"survival"} = survival response, \code{"competing.risks"} =
##' competing risks response
##' @param formula Specify regression coefficients
##' @param intercept For binary outcome the intercept of the logistic regression.
##' @usage
##' sampleData(n,outcome="competing.risks",
##' formula= ~ f(X1,2)+f(X2,-0.033)+f(X3,0.4)+f(X6,.1)+f(X7,-.1)+f(X8,.5)+f(X9,-1),
##'           intercept=0)
##' sampleDataTD(n,n.intervals=5,outcome="competing.risks",
##' formula= ~ f(X1,2)+f(X2,-0.033)+f(X3,0.4)+f(X6,.1)+f(X7,-.1)+f(X8,.5)+f(X9,-1))
##' @return Simulated data as data.table with n rows and the following columns:
##' Y (binary outcome), time (non-binary outcome), event (non-binary outcome),
##' X1-X5 (binary predictors), X6-X10 (continous predictors)
##' @seealso lvm
##' @examples
##' set.seed(10)
##' sampleData(10,outcome="binary")
##' sampleData(10,outcome="survival")
##' sampleData(10,outcome="competing.risks")
##' @export
##' @author Thomas A. Gerds <tag@@biostat.ku.dk>
sampleData <- function(n,
outcome="competing.risks",
formula= ~ f(X1,2) + f(X2,-0.033) + f(X3,0.4) + f(X6,.1) + f(X7,-.1) + f(X8,.5) + f(X9,-1),
intercept=0){
X1=X2=X3=X4=X5=NULL
outcome <- match.arg(outcome,c("survival","competing.risks","binary"))
m <- lava::lvm()
lava::distribution(m,~X6) <- lava::normal.lvm(mean=60,sd=15)
lava::distribution(m,~X7) <- lava::normal.lvm(mean=60,sd=5)
lava::distribution(m,~X8) <- lava::normal.lvm(mean=0,sd=1)
lava::distribution(m,~X9) <- lava::normal.lvm(mean=0,sd=1)
lava::distribution(m,~X10) <- lava::normal.lvm(mean=0,sd=1)
lava::distribution(m,~X1) <- lava::binomial.lvm(p=c(.1))
lava::distribution(m,~X2) <- lava::binomial.lvm(p=c(.2))
lava::distribution(m,~X3) <- lava::binomial.lvm(p=c(.3))
lava::distribution(m,~X4) <- lava::binomial.lvm(p=c(.4))
lava::distribution(m,~X5) <- lava::binomial.lvm(p=c(.5))
if ("binary"%in%outcome){
lava::distribution(m,~Y) <- lava::binomial.lvm()
lava::regression(m) <- stats::update(formula,"Y~.")
lava::intercept(m,~Y) <- intercept
}
if ("survival"%in%outcome){
lava::distribution(m, "eventtime") <- lava::coxWeibull.lvm(scale = 1/100)
lava::distribution(m, "censtime") <- lava::coxWeibull.lvm(scale = 1/100)
m <- lava::eventTime(m, time ~ min(eventtime = 1, censtime = 0),"event")
lava::regression(m) <- stats::update(formula,"eventtime~.")
}
if ("competing.risks"%in%outcome){
lava::distribution(m, "eventtime1") <- lava::coxWeibull.lvm(scale = 1/100)
lava::distribution(m, "eventtime2") <- lava::coxWeibull.lvm(scale = 1/100)
lava::distribution(m, "censtime") <- lava::coxWeibull.lvm(scale = 1/100)
m <- lava::eventTime(m, time ~ min(eventtime1 = 1, eventtime2 = 2, censtime = 0), "event")
lava::regression(m) <- stats::update(formula,"eventtime1~.")
}
out <- data.table::as.data.table(lava::sim(m,n))
out[,X1:=factor(X1,levels=c("0","1"),labels=c("0","1"))]
out[,X2:=factor(X2,levels=c("0","1"),labels=c("0","1"))]
out[,X3:=factor(X3,levels=c("0","1"),labels=c("0","1"))]
out[,X4:=factor(X4,levels=c("0","1"),labels=c("0","1"))]
out[,X5:=factor(X5,levels=c("0","1"),labels=c("0","1"))]
out[]
}

##' @export
sampleDataTD <- function(n,n.intervals=5,outcome="competing.risks",formula= ~ f(X1,2) + f(X2,-0.033) + f(X3,0.4) + f(X6,.1) + f(X7,-.1) + f(X8,.5) + f(X9,-1)){
start=NULL
m <- lava::lvm()
lava::distribution(m,~X6) <- lava::normal.lvm(mean=60,sd=15)
lava::distribution(m,~X7) <- lava::normal.lvm(mean=60,sd=5)
lava::distribution(m,~X8) <- lava::normal.lvm(mean=0,sd=1)
lava::distribution(m,~X9) <- lava::normal.lvm(mean=0,sd=1)
lava::distribution(m,~X10) <- lava::normal.lvm(mean=0,sd=1)
lava::distribution(m,~X1) <- lava::binomial.lvm(p=c(.1))
lava::distribution(m,~X2) <- lava::binomial.lvm(p=c(.2))
lava::distribution(m,~X3) <- lava::binomial.lvm(p=c(.3))
lava::distribution(m,~X4) <- lava::binomial.lvm(p=c(.4))
lava::distribution(m,~X5) <- lava::binomial.lvm(p=c(.5))
lava::distribution(m, "eventtime1") <- lava::coxWeibull.lvm(scale = 1/100)
lava::distribution(m, "eventtime2") <- lava::coxWeibull.lvm(scale = 1/100)
lava::distribution(m, "censtime") <- lava::coxWeibull.lvm(scale = 1/10)
m <- lava::eventTime(m, time ~ min(eventtime1 = 1, eventtime2 = 2, censtime = 0), "event")
lava::regression(m) <- stats::update(formula,"eventtime1~.")
update.mydata <- function(m,data){
start=time=NULL
is.censored <- data\$event==0
d <- lava::sim(m,sum(is.censored))
data.table::setDT(d)
d[,start:=data[is.censored,time]]
## if (any(is.na(d\$start))) browser()
d[,time:=time+data[is.censored,time]]
d
}
dTD <- vector(n.intervals,mode="list")
dTD[[1]] <- data.table::as.data.table(lava::sim(m,n))
dTD[[1]][,start:=0]
for (t in 2:n.intervals){
dTD[[t]] <- update.mydata(m,dTD[[t-1]])
}
rbindlist(dTD)
}

#----------------------------------------------------------------------
### sampleData.R ends here
```

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riskRegression documentation built on March 23, 2022, 5:07 p.m.