# 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. ```r dataLongTimeDep( dataSemiLong, timeColumn, eventColumn, idColumn, timeAsFactor = FALSE ) ``` ## Arguments - `dataSemiLong`: Original data in semi-long format ("class data.frame"). - `timeColumn`: Character giving the column name of the observed times ("character vector"). It is required that the observed times are discrete ("integer vector"). - `eventColumn`: Column name of the event indicator ("character vector"). It is required that this is a binary variable with 1=="event" and 0=="censored". - `idColumn`: Name of column of identification number of persons ("character vector"). - `timeAsFactor`: Should the time intervals be coded as factor ("logical vector")? Default is FALSE. In case of default settings the discrete time intervals are treated as quantitative ("numeric vector"). ## Returns Original data in long format 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 zero 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. 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**. ## Examples ```r # 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$day/30) + 1 names(dataSet1) [7] <- "month" # Convert to long format for time varying effects pbcseqLong <- dataLongTimeDep (dataSemiLong = dataSet1, timeColumn = "month", eventColumn = "status", idColumn = "id") pbcseqLong [pbcseqLong$obj == 1, ] ##################### # Start-stop notation library(survival) ?survival::heart # Assume that time was measured on a discrete scale. # Discrete interval lengths are assumed to vary. intervalLimits <- quantile(heart$stop, probs = seq(0.1, 1, by=0.1)) intervalLimits[length(intervalLimits)] <- intervalLimits[length(intervalLimits)] + 1 heart_disc <- contToDisc(dataShort = heart, timeColumn = "stop", intervalLimits = intervalLimits, equi = FALSE) table(heart_disc$timeDisc) # Conversion to long format heart_disc_long <- dataLongTimeDep(dataSemiLong = heart_disc, timeColumn = "timeDisc", eventColumn = "event", idColumn = "id") head(heart_disc_long) ``` ## References Rdpack::insert_ref(key="tutzModelDisc",package="discSurv") Rdpack::insert_ref(key="fahrmeirDiscSurv",package="discSurv") Rdpack::insert_ref(key="thompsonTreatment",package="discSurv") ## See Also `contToDisc`, `dataLong`, `dataLongCompRisks` ## Author(s) Thomas Welchowski welchow@imbie.meb.uni-bonn.de

dataLongTimeDep function