R: Calculate nonparametric cumulative incidence functions and...

Cuminc {mstate} | R Documentation

## Calculate nonparametric cumulative incidence functions and associated standard errors

### Description

This function computes nonparametric cumulative incidence functions and associated standard errors for each value of a group variable.

### Usage

    Cuminc(time, status, data, group, failcodes, na.status = c("remove", "extra"))

### Arguments

`time` | Either 1) a numeric vector containing the failure times or 2) a string containing the column name indicating these failure times
`status` | Either 1) a numeric, factor or character vector containing the failure codes or 2) a string containing the column name indicating these failure codes
`data` | When appropriate, a data frame containing `time`, `status` and/or `group` variables
`group` | Optionally, name of column in data indicating a grouping variable; cumulative incidence functions are calculated for each value or level of `group`. If missing no groups are considered
`failcodes` | A vector indicating which values of `status` are considered as different causes of failure; other values of `status` are considered as censorings. If missing and `status` is numeric, it is assumed that 0 is censoring and all other values indicate failcodes; if missing and `status` is character or factor, then it is assumed that each of the levels/values of `status` is a cause of failure
`na.status` | One of `"remove"` (default) or `"extra"`, indicating whether subjects with missing cause of failure should be removed or whether missing cause of failure should be treated as a separate cause of failure

### Details

The estimated cumulative incidences are as described in Putter, Fiocco & Geskus (2007); the standard errors are the square roots of the Greenwood variance estimators, see eg. Andersen, Borgan, Gill & Keiding (1993), de Wreede, Fiocco & Putter (2009), and they correspond to the variances in eg. Marubini & Valsecchi (1995). In case of no censoring, the estimated cumulative incidences and variances reduce to simple binomial frequencies and their variances.

### Value

An object of class `"Cuminc"`, which is a data frame containing the estimated failure-free probabilities and cumulative incidences and their standard errors. The names of the dataframe are `time`, `Surv`, `seSurv`, and `cuminc` and `secuminc` followed by the values or levels of the `failcodes`. If `group` was specified, a `group` variable is included with the same name and values/levels as the original grouping variable, and with estimated cumulative incidences (SE) for each value/level of `group`.

Cuminc is now simply a wrapper around survfit of the survival package with type=`"mstate"`, only maintained for backward compatibility. The survfit object is kept as attribute (`attr("survfit")`), and the print, plot and summary functions are simply print, plot and summary applied to the survfit object. Subsetting the `"Cuminc"` object results in subsetting the data frame, not in subsetting the survfit object.

### Author(s)

Hein Putter H.Putter@lumc.nl

### References

Andersen PK, Borgan O, Gill RD, Keiding N (1993). Statistical Models Based on Counting Processes. Springer, New York.

Marubini E, Valsecchi MG (1995). Analysing Survival Data from Clinical Trials and Observational Studies. Wiley, New York.

Putter H, Fiocco M, Geskus RB (2007). Tutorial in biostatistics: Competing risks and multi-state models. Statistics in Medicine 26, 2389–2430.

de Wreede L, Fiocco M, Putter H (2009). The mstate package for estimation and prediction in non- and semi-parametric multi-state models. Submitted. http://www.msbi.nl/multistate.

### Examples

    ### These data were used in Putter, Fiocco & Geskus (2007)
    data(aidssi)
    ci <- Cuminc(time=aidssi$time, status=aidssi$status)
    head(ci); tail(ci)
    ci <- Cuminc(time="time", status="status", data=aidssi, group="ccr5")
    head(ci); tail(ci)

    ### Some fake data
    fake <- data.frame(surv=c(seq(2,10,by=2),seq(1,13,by=3),seq(1,9,by=2),seq(1,13,by=3)),
                        stat=rep(0:3,5),Tstage=c(1:4,rep(1:4,rep(4,4))))
    fake$stat[fake$stat==0 & fake$Tstage==2] <- 3
    fake$stat[fake$stat==3 & fake$Tstage==1] <- 2
    fake
    Cuminc(time="surv", status="stat", data=fake)
    # If we remove all entries with status=0,
    # we should get binomial sample probabilities and corresponding SEs
    fake0 <- fake[fake$stat!=0,]
    Cuminc(time="surv", status="stat", data=fake0)

[Package mstate version 0.3.2 Index]