mets (version 1.3.6)

recreg: Recurrent events regression with terminal event

Description

Fits Ghosh-Lin IPCW Cox-type model

Usage

recreg(
  formula,
  data,
  cause = 1,
  death.code = 2,
  cens.code = 0,
  cens.model = ~1,
  weights = NULL,
  offset = NULL,
  Gc = NULL,
  wcomp = NULL,
  marks = NULL,
  augmentation.type = c("lindyn.augment", "lin.augment"),
  ...
)

Arguments

formula

formula with 'Event' outcome

data

data frame

cause

of interest (1 default)

death.code

codes for death (terminating event, 2 default)

cens.code

code of censoring (0 default)

cens.model

for stratified Cox model without covariates

weights

weights for score equations

offset

offsets for model

Gc

censoring weights for time argument, default is to calculate these with a Kaplan-Meier estimator, should then give G_c(T_i-)

wcomp

weights for composite outcome, so when cause=c(1,3), we might have wcomp=c(1,2).

marks

a mark value can be specified, this is vector from the data-frame where the mark value can be found at all events

augmentation.type

of augmentation when augmentation model is given

...

Additional arguments to lower level funtions

Author

Thomas Scheike

Details

For Cox type model : $$ E(dN_1(t)|X) = \mu_0(t)dt exp(X^T \beta) $$ by solving Cox-type IPCW weighted score equations $$ \int (Z - E(t)) w(t) dN_1(t) $$ where $$w(t) = G(t) (I(T_i \wedge t < C_i)/G_c(T_i \wedge t))$$ and $$E(t) = S_1(t)/S_0(t)$$ and $$S_j(t) = \sum X_i^j w_i(t) \exp(X_i^T \beta)$$.

The iid decomposition of the beta's are on the form $$ \int (Z - E ) w(t) dM_1 + \int q(s)/p(s) dM_c $$ and returned as iid.

Events, deaths and censorings are specified via stop start structure and the Event call, that via a status vector and cause (code), censoring-codes (cens.code) and death-codes (death.code) indentifies these. See example and vignette.

Examples

## data with no ties
library(mets)
data(hfactioncpx12)
hf <- hfactioncpx12
hf$x <- as.numeric(hf$treatment) 
dd <- data.frame(treatment=levels(hf$treatment),id=1)

gl <- recreg(Event(entry,time,status)~treatment+cluster(id),data=hf,cause=1,death.code=2)
summary(gl)
pgl <- predict(gl,dd,se=1); plot(pgl,se=1)

## censoring stratified after treatment 
gls <- recreg(Event(entry,time,status)~treatment+cluster(id),data=hf,
cause=1,death.code=2,cens.model=~strata(treatment))
summary(gls)

## IPCW at 2 years 
ll2 <- recregIPCW(Event(entry,time,status)~treatment+cluster(id),data=hf,
cause=1,death.code=2,time=2,cens.model=~strata(treatment))
summary(ll2)