# Performs Cox Proportional Hazards regression using the omnibus function with multiple column realizations `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 ```r RunCoxRegression_Omnibus_Multidose( df, time1 = "start", time2 = "end", event0 = "event", names = c("CONST"), term_n = c(0), tform = "loglin", keep_constant = c(0), a_n = c(0), modelform = "M", fir = 0, der_iden = 0, 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 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 - `der_iden`: number for the subterm to test derivative at, only used for testing runs with a single varying parameter, should be smaller than total number of parameters. indexed starting at 0 - `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 system of linear constraints, formatted as matrix - `cons_vec`: Vector containing constants for system of linear constraints, formatted as vector ## Returns returns a list of the final results for each realization ## 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), "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" fir <- 0 der_iden <- 0 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, "change_all" = TRUE, "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, fir = fir, der_iden = der_iden, realization_columns = realization_columns, realization_index = realization_index, control = control, strat_col = "fac", model_control = list(), cens_weight = "null" ) ``` ## See Also Other Cox Wrapper Functions: `RunCoxNull()`, `RunCoxRegression()`, `RunCoxRegression_Basic()`, `RunCoxRegression_CR()`, `RunCoxRegression_Guesses_CPP()`, `RunCoxRegression_Omnibus()`, `RunCoxRegression_STRATA()`, `RunCoxRegression_Single()`, `RunCoxRegression_Tier_Guesses()`

RunCoxRegression_Omnibus_Multidose function