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Performs Cox Proportional Hazards regression using the omnibus function with multiple column realizations

Source: R/Cox_Regression.R
RunCoxRegression_Omnibus_Multidose.Rd

RunCoxRegression_Omnibus_Multidose uses user provided data, time/event columns, vectors specifying the model, and options to control the convergence and starting positions. Used for 2DMC column uncertainty methods. Returns optimized parameters, log-likelihood, and standard deviation for each realization. Has additional options for using stratification, multiplicative loglinear 1-term, competing risks, and calculation without derivatives

Usage

RunCoxRegression_Omnibus_Multidose(
  df,
  time1 = "%trunc%",
  time2 = "%trunc%",
  event0 = "event",
  names = c("CONST"),
  term_n = c(0),
  tform = "loglin",
  keep_constant = c(0),
  a_n = c(0),
  modelform = "M",
  realization_columns = matrix(c("temp00", "temp01", "temp10", "temp11"), nrow = 2),
  realization_index = c("temp0", "temp1"),
  control = list(),
  strat_col = "null",
  cens_weight = "null",
  model_control = list(),
  cons_mat = as.matrix(c(0)),
  cons_vec = c(0)
)

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

realization_columns

used for multi-realization regressions. Matrix of column names with rows for each column with realizations, columns for each realization

realization_index

used for multi-realization regressions. Vector of column names, one for each column with realizations. Each name should be used in the "names" variable in the equation definition

control

list of parameters controlling the convergence, see Def_Control() for options or vignette("Control_Options")

strat_col

column to stratify by if needed

cens_weight

column containing the row weights

model_control

controls which alternative model options are used, see Def_model_control() for options and vignette("Control_Options") for further details

cons_mat

Matrix containing coefficients for a system of linear constraints, formatted as matrix

cons_vec

Vector containing constants for a system of linear constraints, formatted as vector

Value

returns a list of the final results for each realization

See also

Other Cox Wrapper Functions: CoxCurveSolver(), RunCaseControlRegression_Omnibus(), RunCoxNull(), RunCoxRegression(), RunCoxRegression_Basic(), RunCoxRegression_CR(), RunCoxRegression_Guesses_CPP(), RunCoxRegression_Omnibus(), RunCoxRegression_Single(), RunCoxRegression_Strata(), RunCoxRegression_Tier_Guesses()

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),
  "t0" = c(18, 20, 18, 19, 21, 20, 18),
  "t1" = c(30, 45, 57, 47, 36, 60, 55),
  "lung" = c(0, 0, 1, 0, 1, 0, 0),
  "dose" = c(0, 1, 1, 0, 1, 0, 1)
)
set.seed(3742)
df$rand <- floor(runif(nrow(df), min = 0, max = 5))
df$rand0 <- floor(runif(nrow(df), min = 0, max = 5))
df$rand1 <- floor(runif(nrow(df), min = 0, max = 5))
df$rand2 <- floor(runif(nrow(df), min = 0, max = 5))
time1 <- "t0"
time2 <- "t1"
names <- c("dose", "rand")
term_n <- c(0, 0)
tform <- c("loglin", "loglin")
realization_columns <- matrix(c("rand0", "rand1", "rand2"), nrow = 1)
realization_index <- c("rand")
keep_constant <- c(1, 0)
a_n <- c(0, 0)
modelform <- "M"
cens_weight <- c(0)
event <- "lung"
a_n <- c(-0.1, -0.1)
keep_constant <- c(0, 0)
control <- list(
  "ncores" = 2, "lr" = 0.75, "maxiter" = 1,
  "halfmax" = 2, "epsilon" = 1e-6,
  "deriv_epsilon" = 1e-6, "abs_max" = 1.0,
  "dose_abs_max" = 100.0,
  "verbose" = 0, "ties" = "breslow", "double_step" = 1
)
e <- RunCoxRegression_Omnibus_Multidose(df, time1, time2, event,
  names,
  term_n = term_n, tform = tform,
  keep_constant = keep_constant, a_n = a_n,
  modelform = modelform,
  realization_columns = realization_columns,
  realization_index = realization_index,
  control = control, strat_col = "fac",
  model_control = list(), cens_weight = "null"
)