riskRegression (version 2022.03.22)

iidCox: Extract iid decomposition from a Cox model

Description

Compute the influence function for each observation used to estimate the model

Usage

iidCox(
  object,
  newdata,
  baseline.iid,
  tau.hazard,
  tau.max,
  store.iid,
  keep.times,
  return.object
)
# S3 method for coxph
iidCox(
  object,
  newdata = NULL,
  baseline.iid = TRUE,
  tau.hazard = NULL,
  tau.max = NULL,
  store.iid = "full",
  keep.times = TRUE,
  return.object = TRUE
)
# S3 method for cph
iidCox(
  object,
  newdata = NULL,
  baseline.iid = TRUE,
  tau.hazard = NULL,
  tau.max = NULL,
  store.iid = "full",
  keep.times = TRUE,
  return.object = TRUE
)
# S3 method for phreg
iidCox(
  object,
  newdata = NULL,
  baseline.iid = TRUE,
  tau.hazard = NULL,
  tau.max = NULL,
  store.iid = "full",
  keep.times = TRUE,
  return.object = TRUE
)
# S3 method for CauseSpecificCox
iidCox(
  object,
  newdata = NULL,
  baseline.iid = TRUE,
  tau.hazard = NULL,
  tau.max = NULL,
  store.iid = "full",
  keep.times = TRUE,
  return.object = TRUE
)

Value

For Cox models, it returns the object with an additional iid slot (i.e. object$iid). It is a list containing:

• IFbetaInfluence function for the regression coefficient.

• IFhazardTime differential of the influence function of the hazard.

• IFcumhazardInfluence function of the cumulative hazard.

• calcIFhazardElements used to compute the influence function at a given time.

• timeTimes at which the influence function has been evaluated.

• etime1.minTime of first event (i.e. jump) in each strata.

• etime.maxLast observation time (i.e. jump or censoring) in each strata.

• indexObsIndex of the observation in the original dataset.

For Cause-Specific Cox models, it returns the object with an additional iid slot for each model (e.g. object$models[[1]]iid).

Arguments

object

object The fitted Cox regression model object either obtained with coxph (survival package) or cph (rms package).

newdata

[data.frame] Optional new data at which to do iid decomposition

baseline.iid

[logical] Should the influence function for the baseline hazard be computed.

tau.hazard

[numeric vector] the vector of times at which the i.i.d decomposition of the baseline hazard will be computed

tau.max

[numeric] latest time at which the i.i.d decomposition of the baseline hazard will be computed. Alternative to tau.hazard.

store.iid

[character] the method used to compute the influence function and the standard error. Can be "full" or "minimal". See the details section.

keep.times

[logical] If TRUE add the evaluation times to the output.

return.object

[logical] If TRUE return the object where the iid decomposition has been added. Otherwise return a list (see the return section)

Details

This function implements the first three formula (no number,10,11) of the subsection "Empirical estimates" in Ozenne et al. (2017). If there is no event in a strata, the influence function for the baseline hazard is set to 0.

Argument store.iid: If n denotes the sample size, J the number of jump times, and p the number of coefficients:

• store.iid="full" exports the influence function for the coefficients and the baseline hazard at each event time.

• store.iid="minimal" exports the influence function for the coefficients. For the baseline hazard it only computes the quantities necessary to compute the influence function in order to save memory.

More details can be found in appendix B of Ozenne et al. (2017).

References

Brice Ozenne, Anne Lyngholm Sorensen, Thomas Scheike, Christian Torp-Pedersen and Thomas Alexander Gerds. riskRegression: Predicting the Risk of an Event using Cox Regression Models. The R Journal (2017) 9:2, pages 440-460.

Examples

library(survival)
library(data.table)
library(prodlim)
set.seed(10)
d <- sampleData(100, outcome = "survival")[,.(eventtime,event,X1,X6)]
setkey(d, eventtime)

m.cox <- coxph(Surv(eventtime, event) ~ X1+X6, data = d, y = TRUE, x = TRUE)
system.time(IF.cox <- iidCox(m.cox))

IF.cox.all <- iidCox(m.cox, tau.hazard = sort(unique(c(7,d$eventtime))))
IF.cox.beta <- iidCox(m.cox, baseline.iid = FALSE)