discSurv (version 1.4.2)

dataLongCompRisksTimeDep: Data Long Competing Risks Time Dependent Covariates Transformation

Description

Transforms short data format to long format for discrete survival modelling in the case of competing risks with right censoring. Covariates may vary over time.

Usage

dataLongCompRisksTimeDep(dataSet, timeColumn, eventColumns, 
eventColumnsAsFactor=FALSE, idColumn, timeAsFactor=TRUE)

Arguments

dataSet

Original data in short format. Must be of class "data.frame".

timeColumn

Character giving the column name of the observed times. It is required that the observed times are discrete (integer).

eventColumns

Character vector giving the column names of the event indicators (excluding censoring column). It is required that all events are binary encoded. If the sum of all event indicators is zero, then this is interpreted as a censored observation. Alternatively a column name of a factor representing competing events can be given. In this case the argument "eventColumnsAsFactor" has to be set TRUE and the first level is assumed to represent censoring.

eventColumnsAsFactor

Should the argument eventColumns be intepreted as column name of a factor variable(logical scalar)? Default is FALSE.

idColumn

Name of column of identification number of persons as character.

timeAsFactor

Should the time intervals be coded as factor? Default is to use factor. If the argument is false, the column is coded as numeric.

Value

Original data set in long format with additional columns

• obj: Gives identification number of objects (row index in short format) (integer)

• timeInt: Gives number of discrete time intervals (factor)

• responses: Columns with dimension count of events + 1 (censoring)

  • e0: No event (observation censored in specific interval)

  • e1: Indicator of first event, 1 if event takes place and 0 otherwise

  • ... ...

  • ek: Indicator of last k-th event, 1 if event takes place and 0 otherwise

Details

There may be some intervals, where no additional information on the covariates is observed (e. g. observed values in interval one and three but two is missing). In this case it is assumed, that the values from the last observation stay constant over time until a new measurement was done.

References

Ludwig Fahrmeir, (1997), Discrete failure time models, LMU Sonderforschungsbereich 386, Paper 91, https://epub.ub.uni-muenchen.de/

W. A. Thompson Jr., (1977), On the Treatment of Grouped Observations in Life Studies, Biometrics, Vol. 33, No. 3

See Also

contToDisc, dataLong, dataLongCompRisks

Examples

# NOT RUN {
# Example Primary Biliary Cirrhosis data
library(survival)

# Convert to months
pbcseq$day <- ceiling(pbcseq$day/30)+1
names(pbcseq) [7] <- "month"
pbcseq$status <- factor(pbcseq$status)

# Convert to long format for time varying effects
pbcseqLong <- dataLongCompRisksTimeDep(dataSet=pbcseq, timeColumn="month", 
eventColumns="status", eventColumnsAsFactor=TRUE, idColumn="id", 
timeAsFactor=TRUE)
head(pbcseqLong)
# }