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Workaround statistics function to add HR with CI

Source: R/coxph_hr.R
coxph_hr.Rd

This is a workaround for tern::s_coxph_pairwise(), which adds a statistic containing the hazard ratio estimate together with the confidence interval.

Usage

a_coxph_hr(
  df,
  .var,
  ref_path,
  .spl_context,
  ...,
  .stats = NULL,
  .formats = NULL,
  .labels = NULL,
  .indent_mods = NULL
)

s_coxph_hr(
  df,
  .ref_group,
  .in_ref_col,
  .var,
  is_event,
  strata = NULL,
  control = control_coxph(),
  alternative = c("two.sided", "less", "greater")
)

Arguments

df

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

.var

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

ref_path

(character)
global reference group specification, see get_ref_info().

.spl_context

(data.frame)
gives information about ancestor split states that is passed by rtables.

...

additional arguments for the lower level functions.

.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.

.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.

is_event

(character)
variable name storing Logical values: TRUE if event, FALSE if time to event is censored.

strata

(character or NULL)
variable names indicating stratification factors.

control

(list)
relevant list of control options.

alternative

(string)
whether two.sided, or one-sided less or greater p-value should be displayed.

Value

for s_coxph_hr a list containing the same statistics returned by tern::s_coxph_pairwise and the additional lr_stat_df statistic. for a_coxph_hr, a VerticalRowsSection object.

Functions

  • a_coxph_hr(): Formatted analysis function which is used as afun.

  • s_coxph_hr(): Statistics function forked from tern::s_coxph_pairwise(). the difference is that:

    1. It returns the additional statistic lr_stat_df (log rank statistic with degrees of freedom).

Examples

library(dplyr)

adtte_f <- tern::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() |>
  analyze("AVAL",
    afun = s_coxph_hr,
    extra_args = list(is_event = "is_event"),
    var_labels = "Unstratified Analysis",
    show_labels = "visible"
  ) |>
  build_table(df = adtte_f)
#>                           ARM A                            ARM B                                                  ARM C                        
#>                           (N=69)                          (N=73)                                                  (N=58)                       
#> ———————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————
#> Unstratified Analysis                                                                                                                          
#>   p-value (log-rank)                                0.0904951081206286                                     0.00860286392158099                 
#>   lr_stat_df                                        2.86554400612335, 1                                    6.90346948570891, 1                 
#>   hr                                                 1.40675529802996                                        1.81062231929339                  
#>   95% CI                                    0.945772426764327, 2.09242774745061                     1.15592003553536, 2.83614184575062         
#>   Hazard Ratio (95% CI)            1.40675529802996, 0.945772426764327, 2.09242774745061   1.81062231929339, 1.15592003553536, 2.83614184575062
#>   n_tot                                                     142                                                    127                         
#>   n_tot_events                                              101                                                     84                         

basic_table() |>
  split_cols_by(var = "ARMCD", ref_group = "ARM A") |>
  add_colcounts() |>
  analyze("AVAL",
    afun = s_coxph_hr,
    extra_args = list(
      is_event = "is_event",
      strata = "SEX",
      control = tern::control_coxph(pval_method = "wald")
    ),
    var_labels = "Unstratified Analysis",
    show_labels = "visible"
  ) |>
  build_table(df = adtte_f)
#>                           ARM A                           ARM B                                                  ARM C                        
#>                           (N=69)                          (N=73)                                                 (N=58)                       
#> ——————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————
#> Unstratified Analysis                                                                                                                         
#>   p-value (wald)                                    0.0783698773203908                                    0.00662292473338187                 
#>   lr_stat_df                                       3.12966155715821, 1                                    7.60136269697425, 1                 
#>   hr                                                 1.43602612933735                                       1.88581879789238                  
#>   95% CI                                    0.959767646673475, 2.1486148770351                     1.19297959598155, 2.98103383365769         
#>   Hazard Ratio (95% CI)            1.43602612933735, 0.959767646673475, 2.1486148770351   1.88581879789238, 1.19297959598155, 2.98103383365769
#>   n_tot                                                    142                                                    127                         
#>   n_tot_events                                             101                                                     84                         
adtte_f <- tern::tern_ex_adtte |>
  dplyr::filter(PARAMCD == "OS") |>
  dplyr::mutate(is_event = CNSR == 0)
df <- adtte_f |> dplyr::filter(ARMCD == "ARM A")
df_ref <- adtte_f |> dplyr::filter(ARMCD == "ARM B")

s_coxph_hr(
  df = df,
  .ref_group = df_ref,
  .in_ref_col = FALSE,
  .var = "AVAL",
  is_event = "is_event",
  strata = NULL
)
#> $pvalue
#> [1] 0.09049511
#> attr(,"label")
#> [1] "p-value (log-rank)"
#> 
#> $lr_stat_df
#> [1] 2.865544 1.000000
#> 
#> $hr
#> [1] 0.7108557
#> 
#> $hr_ci
#> [1] 0.4779138 1.0573368
#> attr(,"label")
#> [1] "95% CI"
#> 
#> $hr_ci_3d
#> [1] 0.7108557 0.4779138 1.0573368
#> attr(,"label")
#> [1] "Hazard Ratio (95% CI)"
#> 
#> $n_tot
#> [1] 142
#> 
#> $n_tot_events
#> [1] 101
#>