Cox models are the most commonly used methods to estimate the magnitude of the effect in survival analyses. It assumes proportional hazards; that is, it assumes that the ratio of the hazards of the two groups (e.g. two arms) is constant over time. This ratio is referred to as the "hazard ratio" and is one of the most commonly reported metrics to describe the effect size in survival analysis.
Usage
coxt01_main(
adam_db,
arm_var = "ARM",
time_var = "AVAL",
event_var = "EVENT",
covariates = c("SEX", "RACE", "AAGE"),
strata = NULL,
lbl_vars = "Effect/Covariate Included in the Model",
multivar = FALSE,
...
)
coxt01_pre(adam_db, arm_var = "ARM", ...)
coxt01_post(tlg, prune_0 = FALSE, ...)
coxt01
Arguments
- adam_db
(
list
ofdata.frames
) object containing theADaM
datasets- arm_var
(
string
) the arm variable used for arm splitting.- time_var
(
string
) the time variable in a Cox proportional hazards regression model.- event_var
(
string
) the event variable in a Cox proportional hazards regression model.- covariates
(
character
) will be fitted and the corresponding effect will be estimated.- strata
(
character
) will be fitted for the stratified analysis.- lbl_vars
(
string
) text label for the a Cox regression model variables.- multivar
(
flag
) indicator of whether multivariate cox regression is conducted.- ...
Further arguments passed to
tern::control_coxreg()
.- tlg
(
TableTree
,Listing
orggplot
) object typically produced by amain
function.- prune_0
(
flag
) remove 0 count rows
Value
the main function returns an rtables
object
the preprocessing function returns a list
of data.frame
.
the postprocessing function returns an rtables
object or an ElementaryTable
(null report).
Details
The reference arm will always the first level of
arm_var
. Please change the level if you want to change the reference arms.The table allows confidence level to be adjusted, default is two-sided 95%.
The stratified analysis is with DISCRETE tie handling (equivalent to
tern::control_coxreg(ties = "exact")
in R).Model includes treatment plus specified covariate(s) as factor(s) or numeric(s), with
"SEX"
,"RACE"
and"AAGE"
as default candidates.The selection of the covariates and whether or not there is a selection process (vs. a fixed, pre-specified list) needs to be pre-specified.
For pairwise comparisons using the hazard ratio, the value for the control group is the denominator.
Keep zero-count rows unless overridden with
prune_0 = TRUE
.
Functions
coxt01_main()
: Main TLG functioncoxt01_pre()
: Preprocessingcoxt01_post()
: Postprocessing
Note
adam_db
object must contain anadtte
table with"PARAMCD"
,"ARM"
,"AVAL"
,"CNSR
, and the columns specified by"covariates"
which is denoted asc("SEX", "RACE", "AAGE")
by default.
Examples
library(dunlin)
proc_data <- log_filter(syn_data, PARAMCD == "CRSD", "adtte")
proc_data <- log_filter(proc_data, ARMCD != "ARM C", "adsl")
run(coxt01, proc_data)
#> Treatment Effect Adjusted for Covariate
#> Effect/Covariate Included in the Model n Hazard Ratio 95% CI p-value
#> —————————————————————————————————————————————————————————————————————————————————————————
#> Treatment:
#> B: Placebo vs control (A: Drug X) 268 1.43 (1.06, 1.94) 0.0204
#> Covariate:
#> Sex 268 1.43 (1.06, 1.94) 0.0208
#> RACE 268 1.44 (1.06, 1.96) 0.0208
#> Age (yr) 268 1.46 (1.07, 1.98) 0.0154
run(coxt01, proc_data, covariates = c("SEX", "AAGE"), strata = c("RACE"), conf_level = 0.90)
#> Treatment Effect Adjusted for Covariate
#> Effect/Covariate Included in the Model n Hazard Ratio 90% CI p-value
#> —————————————————————————————————————————————————————————————————————————————————————————
#> Treatment:
#> B: Placebo vs control (A: Drug X) 268 1.42 (1.09, 1.84) 0.0274
#> Covariate:
#> Sex 268 1.42 (1.09, 1.84) 0.0273
#> Age (yr) 268 1.44 (1.11, 1.87) 0.0211