Log-rank test — wrapper_log_rank_test_core

Log-rank test

Usage

wrapper_log_rank_test_core_simple(
  data,
  tte_var,
  censor_var,
  covariate_var,
  strata_vars = NULL,
  keep_obs = TRUE,
  variable_names = NULL,
  caption = NULL,
  sr_times = NULL,
  print_nevent = TRUE,
  print_mst = TRUE,
  print_hr = TRUE,
  print_total = FALSE,
  print_pvalues = TRUE
)

wrapper_log_rank_test_core_simple_strat(
  data,
  tte_var,
  censor_var,
  covariate_var,
  strata_vars = NULL,
  strat1_var = NULL,
  strat2_var = NULL,
  variable_names = NULL,
  caption = NULL,
  sr_times = NULL,
  print_nevent = TRUE,
  print_mst = TRUE,
  print_hr = TRUE,
  print_total = TRUE,
  print_pvalues = TRUE,
  print_adjpvalues = TRUE
)

Arguments

data: Data frame.
tte_var: Name of the time-to-event variable. This variable must be numeric.
censor_var: Name of the censor variable. It has to be numeric and encode 1 for event and 0 for censor.
covariate_var: Name of covariate that defines subgroups where the survival is estimated.
strata_vars: Vector with names of covariates that are used as strata.
variable_names: Named vector with variable names. If not supplied, variable names are created by replacing in column names underscores with spaces.
caption: Caption for the table with results.
print_nevent: Logical. Whether to print numbers of events.
print_mst: Logical. Whether to print median survival time (MST).
print_hr: Logical. Whether to print hazard rations estimated with Cox regression.
print_total: Logical. Whether to print total number of samples and total number of events.
print_pvalues: Logical. Whether to print p-values.
strat1_var: Name of the firts stratification variable.
strat2_var: Name of the second stratification variable.
print_adjpvalues: Logical. Whether to print adjusted p-values.

Examples


data(bdata)

data <- bdata
tte_var <- "PFS"
censor_var <- "PFS_Event"
covariate_var <- "Treatment_Arm"

x <- wrapper_log_rank_test_core_simple(data, tte_var = tte_var, censor_var = censor_var, covariate_var = covariate_var)

boutput(x)
#>       Covariate Subgroup   N      Events  MST  MST 95% CI   HR     HR 95% CI
#> 1 Treatment Arm     CTRL 252 109 (43.3%) 83.1 (78.3 - NA)                   
#> 2                    TRT 248 105 (42.3%) 99.9 (87.5 - NA) 0.79 (0.60 - 1.03)
#>    P-value
#> 1 0.0806 .
#> 2         


### Fit a stratified model 

strata_vars <- c("IPI", "Cell_Of_Origin")

x <- wrapper_log_rank_test_core_simple(data, tte_var = tte_var, censor_var = censor_var, covariate_var = covariate_var, strata_vars = strata_vars)

boutput(x)
#>       Covariate Subgroup   N     Events   MST  MST 95% CI   HR     HR 95% CI
#> 1 Treatment Arm     CTRL 223 95 (42.6%)  83.3 (80.3 - NA)                   
#> 2                    TRT 222 90 (40.5%) 100.3 (88.7 - NA) 0.70 (0.52 - 0.94)
#>    P-value
#> 1 0.0167 *
#> 2         



data(bdata)

data <- bdata
tte_var <- "PFS"
censor_var <- "PFS_Event"
covariate_var <- "Treatment_Arm"

strat1_var = "Cell_Of_Origin"


x <- wrapper_log_rank_test_core_simple_strat(data, tte_var = tte_var, censor_var = censor_var, covariate_var = covariate_var, strat1_var = strat1_var)

boutput(x)
#>   Cell Of Origin     Covariate Subgroup Total N Total Events   N     Events
#> 1            GCB Treatment Arm     CTRL     252           91 135 56 (41.5%)
#> 2                                   TRT     252           91 117 35 (29.9%)
#> 3   UNCLASSIFIED Treatment Arm     CTRL      69           37  32 15 (46.9%)
#> 4                                   TRT      69           37  37 22 (59.5%)
#> 5            ABC Treatment Arm     CTRL     124           57  56 24 (42.9%)
#> 6                                   TRT     124           57  68 33 (48.5%)
#>    MST    MST 95% CI   HR     HR 95% CI   P-value Adj. P-value
#> 1 85.6   (78.3 - NA)                    0.0090 **     0.0269 *
#> 2   NA  (100.3 - NA) 0.57 (0.37 - 0.87)                       
#> 3 83.3   (55.1 - NA)                       0.6926       0.6926
#> 4 82.1 (57.4 - 92.1) 1.14 (0.59 - 2.20)                       
#> 5 82.6   (63.6 - NA)                       0.5822       0.6926
#> 6 92.0   (71.9 - NA) 0.86 (0.51 - 1.46)