Cox_Relative_Risk {Colossus}

Calculates hazard ratios for a reference vector

Description

RunCoxRegression uses user provided data, vectors specifying the model, and options to calculate relative risk for every row in the provided data

Usage

Cox_Relative_Risk(
  df,
  time1,
  time2,
  event0,
  names,
  Term_n,
  tform,
  keep_constant,
  a_n,
  modelform,
  fir,
  control,
  model_control = list()
)

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

control

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

model_control

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

Value

returns a list of the final results

See Also

Other Plotting Wrapper Functions: RunCoxPlots()

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)
fir <- 0
tform <- c("loglin","lin","lin","plin")
modelform <- "M"
a_n <- c(1.1, 0.1, 0.2, 0.5) #used to test at a specific point

keep_constant <- c(0,0,0,0)

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)

e <- Cox_Relative_Risk(df, time1, time2, event, names, Term_n, tform,
     keep_constant, a_n, modelform, fir, control)