bootCRCumInc {hrcomprisk}

Bootstrap for Ratios of Hazard Ratios

Description

Bootstrap 95% Confidence Intervals limits for estimated Ratios of sHR/csHR.

Usage

bootCRCumInc(
  df,
  exit,
  event,
  exposure,
  entry = NULL,
  weights = NULL,
  ipwvars = NULL,
  rep = 0,
  print.attr = T,
  seed = 54321
)

Arguments

df

A data frame containing, at a minimum, exit, event, and exposure.

exit

Name of the column in df containing times of event or censoring.

event

Name of the column in df containing codes for censoring (0) and event types (1-4). Analysis of more than 4 competing events is not supported by this function.

exposure

Name of the column in df containing a binary (0/1) exposure variable for stratification.

entry

Name of the column in df containing late entry times.

weights

Name of the column in df containing user-supplied weights. If ipwvars is utilized, this argument is ignored.

ipwvars

A vector of names of columns in 'df' containing predictor variables for building a propensity score model for exposure and creating standardized inverse probability weights using this model. Overrides the weights argument.

rep

Number of replicates for bootstrapping if confidence intervals for the sHR/csHR estimate are desired. See more details on bootstrapping below.

print.attr

A logical indicator for whether results should be returned in console.

seed

A seed number start for the bootstrap estimation.

Value

A data frame with the 95% confidence interval limits (upper and lower) for Sub-hazard ratio/Cause-specific hazard ratio for each event:

R1.lower

Lower limit of the 95%CI of the Sub-hazard ratio/Cause-specific hazard ratio for event 1 at time t

R1.upper

Upper limit of the 95%CI of the Sub-hazard ratio/Cause-specific hazard ratio for event 1 at time t

R2.lower

Lower limit of the 95%CI of the Sub-hazard ratio/Cause-specific hazard ratio for event 2 at time t

R2.upper

Upper limit of the 95%CI of the Sub-hazard ratio/Cause-specific hazard ratio for event 2 at time t

Examples

#data from the package
data <- hrcomprisk::dat_ckid
#Obtain the 95%CI by bootstraping
ciCIF<-bootCRCumInc(df=data, exit=exit, event=event, exposure=b1nb0, rep=10, print.attr=TRUE)