eventTime: Add an observed event time outcome to a latent variable...

Description Usage Arguments Details Author(s) Examples

Description

For example, if the model 'm' includes latent event time variables are called 'T1' and 'T2' and 'C' is the end of follow-up (right censored), then one can specify

Usage

1
eventTime(object, formula, eventName, ...)

Arguments

object

Model object

formula

Formula (see details)

eventName

Event names

...

Additional arguments to lower levels functions

Details

eventTime(object=m,formula=ObsTime~min(T1=a,T2=b,C=0,"ObsEvent"))

when data are simulated from the model one gets 2 new columns:

- "ObsTime": the smallest of T1, T2 and C - "ObsEvent": 'a' if T1 is smallest, 'b' if T2 is smallest and '0' if C is smallest

Note that "ObsEvent" and "ObsTime" are names specified by the user.

Author(s)

Thomas A. Gerds

Examples

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# Right censored survival data without covariates
m0 <- lvm()
distribution(m0,"eventtime") <- coxWeibull.lvm(scale=1/100,shape=2)
distribution(m0,"censtime") <- coxExponential.lvm(rate=10)
m0 <- eventTime(m0,time~min(eventtime=1,censtime=0),"status")
sim(m0,10)

# Alternative specification of the right censored survival outcome
## eventTime(m,"Status") <- ~min(eventtime=1,censtime=0)

# Cox regression:
# lava implements two different parametrizations of the same
# Weibull regression model. The first specifies
# the effects of covariates as proportional hazard ratios
# and works as follows:
m <- lvm()
distribution(m,"eventtime") <- coxWeibull.lvm(scale=1/100,shape=2)
distribution(m,"censtime") <- coxWeibull.lvm(scale=1/100,shape=2)
m <- eventTime(m,time~min(eventtime=1,censtime=0),"status")
distribution(m,"sex") <- binomial.lvm(p=0.4)
distribution(m,"sbp") <- normal.lvm(mean=120,sd=20)
regression(m,from="sex",to="eventtime") <- 0.4
regression(m,from="sbp",to="eventtime") <- -0.01
sim(m,6)
# The parameters can be recovered using a Cox regression
# routine or a Weibull regression model. E.g.,
## Not run: 
    set.seed(18)
    d <- sim(m,1000)
    library(survival)
    coxph(Surv(time,status)~sex+sbp,data=d)

    sr <- survreg(Surv(time,status)~sex+sbp,data=d)
    library(SurvRegCensCov)
    ConvertWeibull(sr)


## End(Not run)

# The second parametrization is an accelerated failure time
# regression model and uses the function weibull.lvm instead
# of coxWeibull.lvm to specify the event time distributions.
# Here is an example:

ma <- lvm()
distribution(ma,"eventtime") <- weibull.lvm(scale=3,shape=0.7)
distribution(ma,"censtime") <- weibull.lvm(scale=2,shape=0.7)
ma <- eventTime(ma,time~min(eventtime=1,censtime=0),"status")
distribution(ma,"sex") <- binomial.lvm(p=0.4)
distribution(ma,"sbp") <- normal.lvm(mean=120,sd=20)
regression(ma,from="sex",to="eventtime") <- 0.7
regression(ma,from="sbp",to="eventtime") <- -0.008
set.seed(17)
sim(ma,6)
# The regression coefficients of the AFT model
# can be tranformed into log(hazard ratios):
#  coef.coxWeibull = - coef.weibull / shape.weibull
## Not run: 
    set.seed(17)
    da <- sim(ma,1000)
    library(survival)
    fa <- coxph(Surv(time,status)~sex+sbp,data=da)
    coef(fa)
    c(0.7,-0.008)/0.7

## End(Not run)


# The Weibull parameters are related as follows:
# shape.coxWeibull = 1/shape.weibull
# scale.coxWeibull = exp(-scale.weibull/shape.weibull)
# scale.AFT = log(scale.coxWeibull) / shape.coxWeibull
# Thus, the following are equivalent parametrizations
# which produce exactly the same random numbers:

model.aft <- lvm()
distribution(model.aft,"eventtime") <- weibull.lvm(scale=-log(1/100)/2,shape=0.5)
distribution(model.aft,"censtime") <- weibull.lvm(scale=-log(1/100)/2,shape=0.5)
set.seed(17)
sim(model.aft,6)

model.cox <- lvm()
distribution(model.cox,"eventtime") <- coxWeibull.lvm(scale=1/100,shape=2)
distribution(model.cox,"censtime") <- coxWeibull.lvm(scale=1/100,shape=2)
set.seed(17)
sim(model.cox,6)

# The minimum of multiple latent times one of them still
# being a censoring time, yield
# right censored competing risks data

mc <- lvm()
distribution(mc,~X2) <- binomial.lvm()
regression(mc) <- T1~f(X1,-.5)+f(X2,0.3)
regression(mc) <- T2~f(X2,0.6)
distribution(mc,~T1) <- coxWeibull.lvm(scale=1/100)
distribution(mc,~T2) <- coxWeibull.lvm(scale=1/100)
distribution(mc,~C) <- coxWeibull.lvm(scale=1/100)
mc <- eventTime(mc,time~min(T1=1,T2=2,C=0),"event")
sim(mc,6)


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