binregRatio: Percentage of years lost due to cause regression

In mets: Analysis of Multivariate Event Times

Percentage of years lost due to cause regression

Description

Estimates the percentage of the years lost that is due to a cause and how covariates affects this percentage by doing ICPW regression.

Usage

binregRatio(
  formula,
  data,
  cause = 1,
  time = NULL,
  beta = NULL,
  type = c("II", "I"),
  offset = NULL,
  weights = NULL,
  cens.weights = NULL,
  cens.model = ~+1,
  se = TRUE,
  kaplan.meier = TRUE,
  cens.code = 0,
  no.opt = FALSE,
  method = "nr",
  augmentation = NULL,
  outcome = c("cif", "rmtl"),
  model = c("logit", "exp", "lin"),
  Ydirect = NULL,
  ...
)

Arguments

formula	formula with outcome (see coxph)
data	data frame
cause	cause of interest (numeric variable)
time	time of interest
beta	starting values
type	"II" adds augmentation term, and "I" classical outcome IPCW regression
offset	offsets for partial likelihood
weights	for score equations
cens.weights	censoring weights
cens.model	only stratified cox model without covariates
se	to compute se's based on IPCW
kaplan.meier	uses Kaplan-Meier for IPCW in contrast to exp(-Baseline)
cens.code	gives censoring code
no.opt	to not optimize
method	for optimization
augmentation	to augment binomial regression
outcome	can do CIF regression "cif"=F(t|X), "rmtl"=E( t- min(T, t) | X)"
model	logit, exp or lin(ear)
Ydirect	use this Y instead of outcome constructed inside the program, should be a matrix with two column for numerator and denominator.
...	Additional arguments to lower level funtions

Details

Let the years lost be

Y1= t- min(T ,)

and the years lost due to cause 1

Y2= I(epsilon==1) ( t- min(T ,t)

, then we model the ratio

logit( E(Y2 | X)/E(Y1 | X)) = X^T \beta

. Estimation is based on on binomial regresion IPCW response estimating equation:

X ( \Delta^{ipcw}(t) Y2 expit(X^T \beta) - Y1 ) = 0

where

\Delta^{ipcw}(t) = I((min(t,T)< C)/G_c(min(t,T)-)

is IPCW adjustment of the response

Y(t)= I(T \leq t, \epsilon=1 )

.

(type="I") sovlves this estimating equation using a stratified Kaplan-Meier for the censoring distribution. For (type="II") the default an additional censoring augmentation term

X \int E(Y(t)| T>s)/G_c(s) d \hat M_c

is added.

The variance is based on the squared influence functions that are also returned as the iid component. naive.var is variance under known censoring model.

Censoring model may depend on strata (cens.model=~strata(gX)).

Author(s)

Thomas Scheike

Examples

library(mets)
data(bmt); bmt$time <- bmt$time+runif(408)*0.001

rmst30 <- rmstIPCW(Event(time,cause!=0)~platelet+tcell+age,bmt,time=30,cause=1)
rmst301 <- rmstIPCW(Event(time,cause)~platelet+tcell+age,bmt,time=30,cause=1)
rmst302 <- rmstIPCW(Event(time,cause)~platelet+tcell+age,bmt,time=30,cause=2)

estimate(rmst30)
estimate(rmst301)
estimate(rmst302)

## percentage of total cumulative incidence due to cause 1
rmtlratioI <- rmtlRatio(Event(time,cause)~platelet+tcell+age,bmt,time=30,cause=1)
summary(rmtlratioI)

pp <- predict(rmtlratioI,bmt)
ppb <- cbind(pp,bmt)

## percentage of total cumulative incidence due to cause 1
cifratio <- binregRatio(Event(time,cause)~platelet+tcell+age,bmt,time=30,cause=1)
summary(cifratio)
pp <- predict(cifratio,bmt)

rmtlratioI <- binregRatio(Event(time,cause)~platelet+tcell+age,bmt,
                               time=30,cause=1,outcome="rmtl")
summary(rmtlratioI)

pp <- predict(rmtlratioI,bmt)
ppb <- cbind(pp,bmt)

mets documentation built on Nov. 5, 2025, 5:35 p.m.