GetCensWeight: Calculates and returns data for time by hazard and survival...

In Colossus: "Risk Model Regression and Analysis with Complex Non-Linear Models"

Calculates and returns data for time by hazard and survival to estimate censoring rate

Description

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

Usage

GetCensWeight(
  df,
  time1,
  time2,
  event0,
  names,
  term_n,
  tform,
  keep_constant,
  a_n,
  modelform,
  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 the 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
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

Value

saves the plots in the current directory and returns a data.table of time and corresponding hazard, cumulative hazard, and survival

Examples

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"
a_n <- c(0.1, 0.1, 0.1, 0.1)
keep_constant <- c(0, 0, 0, 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, "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, 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

Colossus documentation built on June 8, 2025, 1:10 p.m.