RunCoxRegression_CR {Colossus}

Performs basic Cox Proportional Hazards regression with competing risks

Description

RunCoxRegression_CR uses user provided data, time/event columns, vectors specifying the model, and options to control the convergence, starting positions, and censoring adjustment

Usage

RunCoxRegression_CR(
  df,
  time1,
  time2,
  event0,
  names,
  Term_n,
  tform,
  keep_constant,
  a_n,
  modelform,
  fir,
  der_iden,
  control,
  cens_weight
)

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

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

control

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

cens_weight

list of weights for censoring rate

Value

returns a list of the final results

See Also

Other Cox Wrapper Functions: RunCoxEventAssignment(), RunCoxNull(), RunCoxRegression(), RunCoxRegression_Basic(), RunCoxRegression_Guesses_CPP(), RunCoxRegression_Omnibus(), RunCoxRegression_STRATA(), RunCoxRegression_Single(), 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),
           "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,   2,   1,   2,   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

control <- list("Ncores"=2,'lr' = 0.75,'maxiter' = 5,'halfmax' = 5,'epsilon' = 1e-3,
   'dbeta_max' = 0.5,'deriv_epsilon' = 1e-3, 'abs_max'=1.0,'change_all'=TRUE,
   'dose_abs_max'=100.0,'verbose'=FALSE, 'ties'='breslow','double_step'=1)
#weights the probability that a row would continue to extend without censoring,
#    for risk group calculation 
cens_weight <- c(0.83, 0.37, 0.26, 0.34, 0.55, 0.23, 0.27)
#censoring weight is generated by the survival library finegray function, or by hand.
#The ratio of weight at event end point to weight at row endpoint is used.
e <- RunCoxRegression_CR(df, time1, time2, event, names, Term_n, tform,
     keep_constant, a_n, modelform, fir, der_iden, control, cens_weight)