Cprob (version 1.2.2)

cpf: Conditional Probability Function of a Competing Event

Description

This function computes estimates of the conditional probability function of a competing event and its variance. It also tests equality of conditional probability functions in two samples.

Usage

cpf(formula, data, subset, na.action, conf.int = 0.95, failcode)

Arguments

formula

A formula object that has a Hist object on the left of a ~ operator, and if desired, terms separated by + on the right. Note that any subsetting, i.e., data$var or data[, "var"], is invalid for this funct

data

A data frame in which the variables in the formula can be interpreted.

subset

Expression identifying a subset of the data to be used for conditional probability estimation.

na.action

A missing-data filter function, applied to the model frame, after any subset argument has been used. Default option is options()$na.action.

conf.int

Level for pointwise two-sided confidence intervals. Default is 0.95.

failcode

Failure code of the event of interest. Default is the smallest event type provided in the data.

Value

• cpf returns an object of class cpf with components
• cp
  Estimates of the conditional probability function given at all event times
• var
  Variance estimates
• time
  Event times
• lower
  Lower confidence limit for the conditional probability curve
• upper
  Upper confidence limit for the conditional probability curve
• n.risk
  Number of individuals at risk just before $t$
• n.event
  A matrix giving the number of events of interest at time $t$ in the first column, and the number of competing events at time $t$ in the second column
• n.lost
  Number of censored observations at time $t$
• size.strata
  Displays the size of each strata
• X
  Gives covariate's name and labels
• strata
  Gives the covariate labels that will be used by default for plotting the conditional probability curves, for example.
• call
  Call that produced the object
• z
  Test statististic
• p
  p value of the test
• failcode
  Same as in function call

Details

The conditional probability function is defined as the probability of having failed due to one competing event (the event of interest), given that no other event has previously occurred (Pepe, 1993).

The cpf function aims at estimating this quantity along with its variance at each event times. It also computes a test of equality of conditional probability curves in two samples (and only in two samples).

Of note, if there is more than 2 competing events, the failure types that are not of interest are aggregated into one competing event.

References

M.S. Pepe and M. Mori, Kaplan-Meier, marginal or conditional probability curves in summarizing competing risks failure time data? Statistics in Medicine, 12(8):737--751. A. Allignol, A. Latouche, J. Yan and J.P. Fine (2011). A regression model for the conditional probability of a competing event: application to monoclonal gammopathy of unknown significance. Journal of the Royal Statistical Society: Series C, 60(1):135--142.

See Also

Hist, print.cpf, summary.cpf, plot.survfit

Examples

data(mgus)

CP <- cpf(Hist(time, ev), data = mgus)
CP

## With age dichotomised according to its median
mgus$AGE <- ifelse(mgus$age < 64, 0, 1)
CP <- cpf(Hist(time, ev)~AGE, data = mgus)
CP
summary(CP)

## Conditional probability of the competing event
CP.death <- cpf(Hist(time, ev), data = mgus, failcode = 2)
CP.death