# Data long transformation for multi spell analysis Transform data from short format into long format for discrete multi spell survival analysis and right censoring. ```r dataLongMultiSpell( dataSemiLong, timeColumn, eventColumn, idColumn, timeAsFactor = FALSE, spellAsFactor = FALSE ) ``` ## Arguments - `dataSemiLong`: Original data in semi-long format ("class data.frame"). - `timeColumn`: Character giving the column name of the observed times. It is required that the observed times are discrete ("character vector"). - `eventColumn`: Column name of the event status ("character vector"). The events can take multiple values on a discrete scale (0, 1, 2, ...) and repetition of events is allowed (integer vector or class factor). It is assumed that the number zero corresponds to censoring and all number > 0 represent the observed states between transitions. - `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"). - `spellAsFactor`: Should the spells be coded as factor ("logical vector")? Default is not to use factor. If the argument is false, the column is coded as numeric. ## Returns Original data.frame with three additional columns: * obj Index of persons as integer vector * timeInt Index of time intervals (factor or integer vector) * spell The spell gives the actual state of each individual within a given discrete interval. * e0 Response transition in long format as binary vector. Column **e0** represents censoring. If **e0** is coded one in the in the last observed time interval **timeInt** of a person, then this observation was censored. * e1 Response in long format as binary vector. The column **e1** represents the transition to the first event state. * eX Response in long format as binary vector. The column **eX** represents the transition to the last event state out of the set of possible states "1, 2, 3, ..., X". * ... Expanded columns of original data set. ## Details If the data has continuous survival times, the response may be transformed to discrete intervals using function `contToDisc`. The discrete time variable needs to be strictly increasing for each person, because otherwise the order of the events is not distinguishable. Here is an example data structure in short format prior augmentation with three possible states: \ idColumn=1, 1, ... , 1, 2, 2, ... , n \ timeColumn= t_ID1_1 < t_ID1_1 < ... < t_ID1_k, t_ID2_1 < t_ID2_2 < ... < t_ID2_k, ... \ eventColumn = 0, 1, ... , 2, 1, 0, ... , 0 The starting state of each individual is assumed to given with time interval equals zero. For example in an illness-death model with three states ("healthy", "illness", "death") if an individual was healthy at the beginning of the study this has to be encoded with discrete time interval set to zero and event state "healthy". ## Examples ```r ################################ # Example with unemployment data data(unempMultiSpell) # Select subsample of first 500 persons unempSub <- unempMultiSpell[unempMultiSpell$id %in% 1:250,] # Expansion from semi-long to long format unempLong <- dataLongMultiSpell(dataSemiLong=unempSub, timeColumn = "year", eventColumn="spell", idColumn="id", spellAsFactor=TRUE, timeAsFactor=FALSE) head(unempLong, 25) # Fit discrete multi-state model regression model library(VGAM) model <- vgam(cbind(e0, e1, e2, e3, e4) ~ 0 + s(timeInt) + age:spell, data = unempLong, family = multinomial(refLevel="e0")) ############################ # Example with artificial data # Seed specification set.seed(-2578) # Construction of data set # Censoring and three possible states (0, 1, 2, 3) # Discrete time intervals (1, 2, ... , 10) # Noninfluential variable x ~ N(0, 1) datFrame <- data.frame( ID = c(rep(1, 6), rep(2, 4), rep(3, 3), rep(4, 2), rep(5, 4), rep(6, 5), rep(7, 7), rep(8, 8)), time = c(c(0, 2, 5, 6, 8, 10), c(0, 1, 6, 7), c(0, 9, 10), c(0, 6), c(0, 2, 3, 4), c(0, 3, 4, 7, 9), c(0, 2, 3, 5, 7, 8, 10), c(0, 1, 3, 4, 6, 7, 8, 9) ), state = c(c(2, 1, 3, 2, 1, 0), c(3, 1, 2, 2), c(2, 2, 1), c(1, 2), c(3, 2, 2, 0), c(1, 3, 2, 1, 3), c(1, 1, 2, 3, 2, 1, 3), c(3, 2, 3, 2, 1, 1, 2, 3) ), x = rnorm(n=6+4+3+2+4+5+7+8) ) # Transformation to long format datFrameLong <- dataLongMultiSpell(dataSemiLong=datFrame, timeColumn="time", eventColumn="state", idColumn="ID", spellAsFactor=TRUE) head(datFrameLong, 25) library(VGAM) cRm <- vglm(cbind(e0, e1, e2, e3) ~ 0 + timeInt + x:spell, data = datFrameLong, family = "multinomial") summary(cRm) ``` ## 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`, `dataLongTimeDep`, `dataLongCompRisks`, `dataLongCompRisks` ## Author(s) Thomas Welchowski welchow@imbie.meb.uni-bonn.de

dataLongMultiSpell function