Support with coxphf model about broom.helpers HOT 9 CLOSED

library(coxphf)
library(broom.helpers)
library(survival)
library(gtsummary)
#> #Uighur
#> 
#> Attachement du package : 'gtsummary'
#> L'objet suivant est masqué depuis 'package:broom.helpers':
#> 
#>     all_continuous
library(tidyverse)

df <- gtsummary::trial

mod <- coxphf(
  Surv(ttdeath, death) ~ age + grade,
  data = df
)
#> Error in cbind(obj$mm1, obj$timedata): le nombre de lignes des matrices doit correspondre (voir argument 2)

df <- df |> tidyr::drop_na(age, grade)

mod <- coxphf(
  Surv(ttdeath, death) ~ age + grade,
  data = df
)

broom::tidy(mod)
#> coxphf(formula = Surv(ttdeath, death) ~ age + grade, data = df)
#> 
#> Model fitted by Penalized ML
#> Confidence intervals and p-values by Profile Likelihood 
#> 
#>                 coef    se(coef) exp(coef) lower 0.95 upper 0.95     Chisq
#> age      0.006553054 0.007024429  1.006575  0.9928443   1.020559 0.8727594
#> gradeII  0.179710038 0.253931543  1.196870  0.7284027   1.966637 0.5074864
#> gradeIII 0.582291957 0.238403535  1.790137  1.1288125   2.866911 6.1274020
#>                   p
#> age      0.35019253
#> gradeII  0.47622901
#> gradeIII 0.01331023
#> 
#> Likelihood ratio test=7.323796 on 3 df, p=0.06226301, n=189
#> Wald test = 7.377032 on 3 df, p = 0.06080364
#> 
#> Covariance-Matrix:
#>                    age       gradeII      gradeIII
#> age       4.934260e-05 -4.848146e-05 -2.820656e-05
#> gradeII  -4.848146e-05  6.448123e-02  3.224237e-02
#> gradeIII -2.820656e-05  3.224237e-02  5.683625e-02
#> Error in co[, -2, drop = FALSE]: nombre de dimensions incorrect
tidy.coxphf(mod)
#> # A tibble: 3 × 5
#>   term     estimate std.error statistic p.value
#>   <chr>       <dbl>     <dbl>     <dbl>   <dbl>
#> 1 age       0.00655   0.00702     0.873  0.350 
#> 2 gradeII   0.180     0.254       0.507  0.476 
#> 3 gradeIII  0.582     0.238       6.13   0.0133

tidy_plus_plus(mod, tidy_fun = tidy.coxphf)
#> # A tibble: 4 × 19
#>   term     variable var_label var_class var_type    var_nlevels contrasts      
#>   <chr>    <chr>    <chr>     <chr>     <chr>             <int> <chr>          
#> 1 age      age      Age       numeric   continuous           NA <NA>           
#> 2 gradeI   grade    Grade     factor    categorical           3 contr.treatment
#> 3 gradeII  grade    Grade     factor    categorical           3 contr.treatment
#> 4 gradeIII grade    Grade     factor    categorical           3 contr.treatment
#> # ℹ 12 more variables: contrasts_type <chr>, reference_row <lgl>, label <chr>,
#> #   n_obs <dbl>, n_event <dbl>, exposure <dbl>, estimate <dbl>,
#> #   std.error <dbl>, statistic <dbl>, p.value <dbl>, conf.low <dbl>,
#> #   conf.high <dbl>

tbl_regression(mod, tidy_fun = coxphf::tidy.coxphf, exponentiate = TRUE) |> 
  as_kable()

df |> 
  select(death, ttdeath, age, grade) |> 
  tbl_uvregression(
    method = coxphf,
    y = Surv(ttdeath, death),
    exponentiate = TRUE,
    tidy_fun = coxphf::tidy.coxphf
  ) |> 
  add_nevent() |> 
  as_kable()