# Calculates and returns data for time by hazard and survival to estimate censoring rate `GetCensWeight` uses user provided data, time/event columns, vectors specifying the model, and options generate an estimate of the censoring rate, plots, and returns the data ```r GetCensWeight( df, time1, time2, event0, names, term_n, tform, keep_constant, a_n, modelform, fir, control, plot_options, model_control = list(), strat_col = "e" ) ``` ## Arguments - `df`: a data.table containing the columns of interest - `time1`: column used for time period starts - `time2`: column used for time period end - `event0`: column used for event status - `names`: columns for elements of the model, used to identify data columns - `term_n`: term numbers for each element of the model - `tform`: list of string function identifiers, used for linear/step - `keep_constant`: binary values to denote which parameters to change - `a_n`: list of initial parameter values, used to determine number of parameters. May be either a list of vectors or a single vector. - `modelform`: string specifying the model type: M, ME, A, PA, PAE, GMIX, GMIX-R, GMIX-E - `fir`: term number for the initial term, used for models of the form T0*f(Ti) in which the order matters - `control`: list of parameters controlling the convergence, see Def_Control() for options or vignette("Control_Options") - `plot_options`: list of parameters controlling the plot options, see RunCoxPlots() for different options - `model_control`: controls which alternative model options are used, see Def_model_control() for options and vignette("Control_Options") for further details - `strat_col`: column to stratify by if needed ## Returns saves the plots in the current directory and returns a data.table of time and corresponding hazard, cumulative hazard, and survival ## Examples ```r library(data.table) ## basic example code reproduced from the starting-description vignette df <- data.table::data.table( "UserID" = c(112, 114, 213, 214, 115, 116, 117), "Starting_Age" = c(18, 20, 18, 19, 21, 20, 18), "Ending_Age" = c(30, 45, 57, 47, 36, 60, 55), "Cancer_Status" = c(0, 0, 1, 0, 1, 0, 0), "a" = c(0, 1, 1, 0, 1, 0, 1), "b" = c(1, 1.1, 2.1, 2, 0.1, 1, 0.2), "c" = c(10, 11, 10, 11, 12, 9, 11), "d" = c(0, 0, 0, 1, 1, 1, 1) ) # For the interval case time1 <- "Starting_Age" time2 <- "Ending_Age" event <- "Cancer_Status" names <- c("a", "b", "c", "d") term_n <- c(0, 1, 1, 2) tform <- c("loglin", "lin", "lin", "plin") modelform <- "M" fir <- 0 a_n <- c(0.1, 0.1, 0.1, 0.1) keep_constant <- c(0, 0, 0, 0) der_iden <- 0 df$censor <- (df$Cancer_Status == 0) event <- "censor" control <- list( "ncores" = 2, "lr" = 0.75, "maxiter" = 20, "halfmax" = 5, "epsilon" = 1e-6, "deriv_epsilon" = 1e-6, "abs_max" = 1.0, "change_all" = TRUE, "dose_abs_max" = 100.0, "verbose" = FALSE, "ties" = "breslow", "double_step" = 1 ) plot_options <- list( "name" = paste(tempfile(), "run_06", sep = ""), "verbose" = FALSE, "studyID" = "studyID", "age_unit" = "years" ) dft <- GetCensWeight( df, time1, time2, event, names, term_n, tform, keep_constant, a_n, modelform, fir, control, plot_options ) t_ref <- dft$t surv_ref <- dft$surv t_c <- df$t1 cens_weight <- approx(t_ref, surv_ref, t_c, rule = 2)$y ```

GetCensWeight function