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Helper function for generating a pairwise Cox-PH table

Source: R/h_km.R
h_tbl_coxph_pairwise.Rd

[Stable]

Create a data.frame of pairwise stratified or unstratified Cox-PH analysis results.

Usage 

h_tbl_coxph_pairwise(
  df,
  variables,
  ref_group_coxph = NULL,
  control_coxph_pw = control_coxph(),
  annot_coxph_ref_lbls = FALSE
)

Arguments

df

(data.frame)
data set containing all analysis variables.

variables

(named list)
variable names. Details are:

  • tte (numeric)
    variable indicating time-to-event duration values.

  • is_event (logical)
    event variable. TRUE if event, FALSE if time to event is censored.

  • arm (factor)
    the treatment group variable.

  • strata (character or NULL)
    variable names indicating stratification factors.

ref_group_coxph

(string or NULL)
level of arm variable to use as reference group in calculations for annot_coxph table. If NULL (default), uses the first level of the arm variable.

control_coxph_pw

(list)
parameters for comparison details, specified using the helper function control_coxph(). Some possible parameter options are:

  • pval_method (string)
    p-value method for testing hazard ratio = 1. Default method is "log-rank", can also be set to "wald" or "likelihood".

  • ties (string)
    method for tie handling. Default is "efron", can also be set to "breslow" or "exact". See more in survival::coxph()

  • conf_level (proportion)
    confidence level of the interval for HR.

annot_coxph_ref_lbls

(flag)
whether the reference group should be explicitly printed in labels for the annot_coxph table. If FALSE (default), only comparison groups will be printed in annot_coxph table labels.

Value

A data.frame containing statistics HR, XX% CI (XX taken from control_coxph_pw), and p-value (log-rank).

Examples 

library(dplyr)

adtte <- tern_ex_adtte %>%
  filter(PARAMCD == "OS") %>%
  mutate(is_event = CNSR == 0)

h_tbl_coxph_pairwise(
  df = adtte,
  variables = list(tte = "AVAL", is_event = "is_event", arm = "ARM"),
  control_coxph_pw = control_coxph(conf_level = 0.9)
)
#>                  HR       90% CI p-value (log-rank)
#> B: Placebo     1.41 (1.01, 1.96)             0.0905
#> C: Combination 1.81 (1.24, 2.64)             0.0086