Analyze a pairwise Cox-PH model

Usage

coxph_pairwise(
  lyt,
  vars,
  strata = NULL,
  control = control_coxph(),
  na_str = default_na_str(),
  nested = TRUE,
  ...,
  var_labels = "CoxPH",
  show_labels = "visible",
  table_names = vars,
  .stats = c("pvalue", "hr", "hr_ci"),
  .formats = NULL,
  .labels = NULL,
  .indent_mods = NULL
)

s_coxph_pairwise(
  df,
  .ref_group,
  .in_ref_col,
  .var,
  is_event,
  strata = NULL,
  strat = lifecycle::deprecated(),
  control = control_coxph()
)

a_coxph_pairwise(
  df,
  .ref_group,
  .in_ref_col,
  .var,
  is_event,
  strata = NULL,
  strat = lifecycle::deprecated(),
  control = control_coxph()
)

Arguments

lyt

(PreDataTableLayouts)
layout that analyses will be added to.

vars

(character)
variable names for the primary analysis variable to be iterated over.

strata

(character or NULL)
variable names indicating stratification factors.

control

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

• pval_method (string)
  p-value method for testing the null hypothesis that hazard ratio = 1. Default method is "log-rank" which comes from survival::survdiff(), can also be set to "wald" or "likelihood" (from survival::coxph()).

• ties (string)
  specifying the 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.

na_str

(string)
string used to replace all NA or empty values in the output.

nested

(flag)
whether this layout instruction should be applied within the existing layout structure _if possible (TRUE, the default) or as a new top-level element (FALSE). Ignored if it would nest a split. underneath analyses, which is not allowed.

...

additional arguments for the lower level functions.

var_labels

(character)
variable labels.

show_labels

(string)
label visibility: one of "default", "visible" and "hidden".

table_names

(character)
this can be customized in the case that the same vars are analyzed multiple times, to avoid warnings from rtables.

.stats

(character)
statistics to select for the table.

Options are: 'pvalue', 'hr', 'hr_ci', 'n_tot', 'n_tot_events'

.formats

(named character or list)
formats for the statistics. See Details in analyze_vars for more information on the "auto" setting.

.labels

(named character)
labels for the statistics (without indent).

.indent_mods

(named integer)
indent modifiers for the labels. Defaults to 0, which corresponds to the unmodified default behavior. Can be negative.

df

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

.ref_group

(data.frame or vector)
the data corresponding to the reference group.

.in_ref_col

(flag)
TRUE when working with the reference level, FALSE otherwise.

.var

(string)
single variable name that is passed by rtables when requested by a statistics function.

is_event

(flag)
TRUE if event, FALSE if time to event is censored.

strat

Please use the strata argument instead.

Value

• coxph_pairwise() returns a layout object suitable for passing to further layouting functions, or to rtables::build_table(). Adding this function to an rtable layout will add formatted rows containing the statistics from s_coxph_pairwise() to the table layout.

• s_coxph_pairwise() returns the statistics:

  • pvalue: p-value to test the null hypothesis that hazard ratio = 1.

  • hr: Hazard ratio.

  • hr_ci: Confidence interval for hazard ratio.

  • n_tot: Total number of observations.

  • n_tot_events: Total number of events.

• a_coxph_pairwise() returns the corresponding list with formatted rtables::CellValue().

Functions

• coxph_pairwise(): Layout-creating function which can take statistics function arguments and additional format arguments. This function is a wrapper for rtables::analyze().

• s_coxph_pairwise(): Statistics function which analyzes HR, CIs of HR, and p-value of a Cox-PH model.

• a_coxph_pairwise(): Formatted analysis function which is used as afun in coxph_pairwise().

Examples

library(dplyr)

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

df <- adtte_f %>% filter(ARMCD == "ARM A")
df_ref_group <- adtte_f %>% filter(ARMCD == "ARM B")

basic_table() %>%
  split_cols_by(var = "ARMCD", ref_group = "ARM A") %>%
  add_colcounts() %>%
  coxph_pairwise(
    vars = "AVAL",
    is_event = "is_event",
    var_labels = "Unstratified Analysis"
  ) %>%
  build_table(df = adtte_f)
#>                         ARM A       ARM B          ARM C    
#>                         (N=69)      (N=73)         (N=58)   
#> ————————————————————————————————————————————————————————————
#> Unstratified Analysis                                       
#>   p-value (log-rank)                0.0905         0.0086   
#>   Hazard Ratio                       1.41           1.81    
#>   95% CI                         (0.95, 2.09)   (1.16, 2.84)

basic_table() %>%
  split_cols_by(var = "ARMCD", ref_group = "ARM A") %>%
  add_colcounts() %>%
  coxph_pairwise(
    vars = "AVAL",
    is_event = "is_event",
    var_labels = "Stratified Analysis",
    strata = "SEX",
    control = control_coxph(pval_method = "wald")
  ) %>%
  build_table(df = adtte_f)
#>                       ARM A       ARM B          ARM C    
#>                       (N=69)      (N=73)         (N=58)   
#> ——————————————————————————————————————————————————————————
#> Stratified Analysis                                       
#>   p-value (wald)                  0.0784         0.0066   
#>   Hazard Ratio                     1.44           1.89    
#>   95% CI                       (0.96, 2.15)   (1.19, 2.98)