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Pairwise CoxPH model

Source: R/survival_coxph_pairwise.R
survival_coxph_pairwise.Rd

[Stable]

Summarize p-value, HR and CIs from stratified or unstratified CoxPH model.

Usage

s_coxph_pairwise(
  df,
  .ref_group,
  .in_ref_col,
  .var,
  is_event,
  strat = NULL,
  control = control_coxph()
)

a_coxph_pairwise(
  df,
  .ref_group,
  .in_ref_col,
  .var,
  is_event,
  strat = NULL,
  control = control_coxph()
)

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

Arguments

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

(logical)
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

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

strat

(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 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.

lyt

(layout)
input layout where analyses will be added to.

vars

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

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)
character for label.

show_labels

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

table_names

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

.stats

(character)
statistics to select for the table.

.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.

Value

  • s_coxph_pairwise() returns the statistics:

    • pvalue: p-value to test HR = 1.

    • hr: Hazard ratio.

    • hr_ci: Confidence interval for hazard ratio.

    • n_tot: Total number of observations.

    • n_tot_events: Total number of events.

  • 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.

Functions

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

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

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

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",
    strat = "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)