cifreg: CIF regression

Description Usage Arguments Details Author(s) Examples

View source: R/cifreg.R

Description

CIF logistic for propodds=1 default CIF Fine-Gray (cloglog) regression for propodds=NULL

Usage

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cifreg(
  formula,
  data = data,
  cause = 1,
  cens.code = 0,
  cens.model = ~1,
  weights = NULL,
  offset = NULL,
  Gc = NULL,
  propodds = 1,
  ...
)

Arguments

formula

formula with 'Event' outcome

data

data frame

cause

of interest

cens.code

code of censoring

cens.model

for stratified Cox model without covariates

weights

weights for FG score equations

offset

offsets for FG model

Gc

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

propodds

1 is logistic model, NULL is fine-gray model

...

Additional arguments to lower level funtions

Details

For FG model:

\int (X - E ) Y_1(t) w(t) dM_1

is computed and summed over clusters and returned multiplied with inverse of second derivative as iid.naive. 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) = ∑ X_i^j Y_{i1}(t) w_i(t) \exp(X_i^T β)

The iid decomposition of the beta's, however, also have a censoring term that is also is computed and added to UUiid (still scaled with inverse second derivative)

\int (X - E ) Y_1(t) w(t) dM_1 + \int q(s)/p(s) dM_c

and returned as iid

For logistic link standard errors are slightly to small since uncertainty from recursive baseline is not considered, so for smaller data-sets it is recommended to use the prop.odds.subdist of timereg that is also more efficient due to use of different weights for the estimating equations. Alternatively, one can also bootstrap the standard errors.

Author(s)

Thomas Scheike

Examples

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## data with no ties
data(bmt,package="timereg")
bmt$time <- bmt$time+runif(nrow(bmt))*0.01
bmt$id <- 1:nrow(bmt)

## logistic link  OR interpretation
ll=cifreg(Event(time,cause)~tcell+platelet+age,data=bmt,cause=1)
summary(ll)
plot(ll)
nd <- data.frame(tcell=c(1,0),platelet=0,age=0)
pll <- predict(ll,nd)
plot(pll)

## Fine-Gray model
fg=cifreg(Event(time,cause)~tcell+platelet+age,data=bmt,cause=1,propodds=NULL)
summary(fg)
plot(fg)
nd <- data.frame(tcell=c(1,0),platelet=0,age=0)
pfg <- predict(fg,nd)
plot(pfg)

sfg <- cifreg(Event(time,cause)~strata(tcell)+platelet+age,data=bmt,cause=1,propodds=NULL)
summary(sfg)
plot(sfg)

### predictions with CI based on iid decomposition of baseline and beta
# fg <- cifreg(Event(time,cause)~tcell+platelet+age,data=bmt,cause=1,propodds=NULL,cox.prep=TRUE)
# Biid <- iid.baseline.cifreg(Biid,time=20)
# FGprediid(Biid,bmt[1:5,])

mets documentation built on Oct. 23, 2020, 5:55 p.m.