dataLongCompRisksTimeDep {discSurv}

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(
  dataSemiLong,
  timeColumn,
  eventColumns,
  eventColumnsAsFactor = FALSE,
  idColumn,
  timeAsFactor = FALSE,
  responseAsFactor = FALSE
)

Arguments

dataSemiLong

Original data in semi-long format ("class data.frame").

timeColumn

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

eventColumns

Character vector giving the column names of the event indicators (excluding censoring column)("character vector"). 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 vector")? Default is FALSE.

idColumn

Name of column of identification number of persons as character("character vector").

timeAsFactor

Should the time intervals be coded as factor ("logical vector")? Default is FALSE. In the default settings the discrete time intervals are treated as quantitative ("numeric vector").

responseAsFactor

Should the response columns be given as factor ("logical vector")? Default is FALSE.

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.

In contrast to continuous survival (see e. g. Surv) the start and stop time notation is not used here. In discrete time survival analysis the only relevant information is to use the stop time. Start time does not matter, because all discrete intervals need to be included in the long data set format to ensure consistent estimation. It is assumed that the supplied data set dataSemiLong contains all repeated measurements of each cluster in semi-long format (e. g. persons). For further information see example Start-stop notation.

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

  If argument responseAsFactor=TRUE, then responses will be coded as factor in one column.

Author(s)

Thomas Welchowski [e-mail]

References

Fahrmeir L (2005). “Discrete Survival-Time Models.” In Encyclopedia of Biostatistics, chapter Survival Analysis. John Wiley \& Sons.

Thompson Jr. WA (1977). “On the Treatment of Grouped Observations in Life Studies.” Biometrics, 33, 463-470.

See Also

contToDisc, dataLong, dataLongCompRisks

Examples

# Example Primary Biliary Cirrhosis data
library(survival)
pbcseq_example <- pbcseq

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

# Convert to long format for time varying effects
pbcseq_exampleLong <- dataLongCompRisksTimeDep(dataSemiLong = pbcseq_example, timeColumn = "month", 
eventColumns = "status", eventColumnsAsFactor = TRUE, idColumn = "id", 
timeAsFactor = TRUE)
head(pbcseq_exampleLong)

#####################
# Start-stop notation

library(survival)
?pbcseq

# Choose subset of patients
subsetID <- unique(pbcseq$id)[1:100]
pbcseq_mod <- pbcseq[pbcseq$id %in% subsetID, ]

# Convert to start stop notation
pbcseq_mod_split <- split(pbcseq_mod, pbcseq_mod$id)
pbcseq_mod_split <- lapply(1:length(pbcseq_mod_split), function(x) {

 cbind(pbcseq_mod_split[[x]], 
 start_time=c(0, pbcseq_mod_split[[x]][ - dim(pbcseq_mod_split[[x]])[1], "day"]),
 stop_time=pbcseq_mod_split[[x]][, "day"])
 
})
pbcseq_mod <- do.call(rbind, pbcseq_mod_split)

# Convert stop time to months
intervalDef <- c(quantile(pbcseq_mod$stop_time, probs = seq(0.1, 0.9, by=0.1)), Inf)
names(pbcseq_mod)
pbcseq_mod <- contToDisc(dataShort = pbcseq_mod, timeColumn = "stop_time", 
                         intervalLimits = intervalDef, equi = FALSE)
pbcseq_mod$status <- factor(pbcseq_mod$status)

# Conversion to data long format
pbcseq_mod_long <- dataLongCompRisksTimeDep(dataSemiLong = pbcseq_mod, timeColumn = "timeDisc", 
                                           eventColumns = "status",
                                          idColumn = "id", 
                                           eventColumnsAsFactor = TRUE, 
                                          responseAsFactor = TRUE,
                                          timeAsFactor = TRUE)
head(pbcseq_mod_long)