Data Long Time Dependent Covariates
Transforms short data format to long format for discrete survival modelling of single event analysis with right censoring. Covariates may vary over time.
dataLongTimeDep(dataSet, timeColumn, censColumn, idColumn, timeAsFactor=TRUE)
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). |
censColumn |
Character giving the column name of the event indicator. It is required that this is a binary variable with 1=="event" and 0=="censored". |
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. |
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.
Original data.frame with three additional columns:
obj: Index of persons as integer vector
timeInt: Index of time intervals (factor)
y: Response in long format as binary vector. 1=="event happens in period timeInt" and 0 otherwise
Thomas Welchowski welchow@imbie.meb.uni-bonn.de
Gerhard Tutz and Matthias Schmid, (2016), Modeling discrete time-to-event data, Springer series in statistics, Doi: 10.1007/978-3-319-28158-2
Ludwig Fahrmeir, (1997), Discrete failure time models, LMU Sonderforschungsbereich 386, Paper 91, http://epub.ub.uni-muenchen.de/
W. A. Thompson Jr., (1977), On the Treatment of Grouped Observations in Life Studies, Biometrics, Vol. 33, No. 3
# Example Primary Biliary Cirrhosis data library(survival) dataSet1 <- pbcseq # Only event death is of interest dataSet1$status [dataSet1$status==1] <- 0 dataSet1$status [dataSet1$status==2] <- 1 table(dataSet1$status) # Convert to months dataSet1$day <- ceiling(dataSet1$day/30)+1 names(dataSet1) [7] <- "month" # Convert to long format for time varying effects pbcseqLong <- dataLongTimeDep (dataSet=dataSet1, timeColumn="month", censColumn="status", idColumn="id") pbcseqLong [pbcseqLong$obj==1, ]
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