变量 X 和测试“fisher.test”的 add_p()' 中的错误，省略了 p 值

Question

I get the error below when I try to use the add_p() function to get a p-value for differences between my by variable (with 10 levels) and a categorical variable with two levels (yes/no). I am not sure how to provide a reproducible example. From the trials data, I imagine my by variable would be the "T Stage" variable with 10 levels, and the categorical variables would be: (1) "Chemotherapy Treatment" with 2 levels, and (2) "Chemotherapy Treatment2" with 4 levels. But here is the code I ran.

library(gtsummary)
library(tidyverse)
miro_def %>% 
  select(mheim, age_dx, time_t1d_yrs, gender, collard, fhist_pandz) %>% 
  tbl_summary(by = mheim, missing = "no",
              type = list(c(gender, collard, fhist_pandz, mheim) ~ "categorical"),
              label = list(gender ~ "Gender", 
                           fhist_pandz ~ "Family history of PD", 
                           age_dx ~ "Age at diagnosis", 
                           time_t1d_yrs ~ "Follow-up(years)")) %>% 
  add_p() %>% 
  # style the output with custom header 
  #modify_header(stat_by = "{level}") %>% 
  # convert to kableExtra as_kable_extra(booktabs = TRUE) %>% 
  # reduce font size to make table fit. # you may also use the `latex_options = "scale_down"` argument here. 
  kable_styling(font_size = 7, latex_options = "scale_down")

However, I do get a p-value for this by variable (10 levels) with other variables (which are continous/numeric)

1. How can I fix this error?

2. In the case where I have the mentioned multilevel by variable and a multilevel (>2 levels) categorical variable, is there something special I should do to get a p-value?

   There was an error in 'add_p()' for variable 'gender' and test 'fisher.test', p-value omitted: Error in stats::fisher.test(data[[variable]], as.factor(data[[by]])): FEXACT error 7(location). LDSTP=18540 is too small for this problem, (pastp=51.2364, ipn_0:=ipoin[itp=150]=215, stp[ipn_0]=40.6787). Increase workspace or consider using 'simulate.p.value=TRUE' There was an error in 'add_p()' for variable 'collard' and test 'fisher.test', p-value omitted: Error in stats::fisher.test(data[[variable]], as.factor(data[[by]])): FEXACT error 7(location). LDSTP=18570 is too small for this problem, (pastp=37.0199, ipn_0:=ipoin[itp=211]=823, stp[ipn_0]=23.0304). Increase workspace or consider using 'simulate.p.value=TRUE' There was an error in 'add_p()' for variable 'fhist_pandz' and test 'fisher.test', p-value omitted: Error in stats::fisher.test(data[[variable]], as.factor(data[[by]])): FEXACT error 7(location). LDSTP=18570 is too small for this problem, (pastp=36.4614, ipn_0:=ipoin[itp=58]=1, stp[ipn_0]=31.8106). Increase workspace or consider using 'simulate.p.value=TRUE'

Answer 1

since nobody posted an answer, here's what I used when coming across this. Following the Examples given in the help file ?gtsummary::add_p.tbl_summary , I composed a custom function that runs fisher.test with the simulate.p.values = TRUE option:

## define custom test
fisher.test.simulate.p.values <- function(data, variable, by, ...) {
  result <- list()
  test_results <- stats::fisher.test(data[[variable]], data[[by]], simulate.p.value = TRUE)
  result$p <- test_results$p.value
  result$test <- test_results$method
  result
}

## add p-values to your gtsummary table, using custom test defined above
summary_table %>%
add_p(
  test = list(all_categorical() ~ "fisher.test.simulate.p.values")  # this applies the custom test to all categorical variables
) 

You can also amend the number of iterations for computing the simulated p-values by changing the default B = 2000 parameter to fisher.test() above.

All this assumes, of course, that it's appropriate to use Fisher's test in the first place.

Answer 2

Since it fixed the issue for me, I would like to indicate that since version 1.3.6 of gtsummary there is an option in add_p() with which you can specify arguments to the test functions (ie test.args ). Thank you to the developers for this!

From the NEWS :
Each add_p() method now has the test.args = argument . Use this argument to pass additional arguments to the statistical method, eg

add_p(test = c(age, marker) ~ "t.test",
      test.args = c(age, marker) ~ list(var.equal = TRUE))

It is also explained in the add_p() help (ie ?add_p ).

Answer 3

I had a similar problem. You have to increase your workspace with test.args within add_p() .

miro_def %>% 
  select(mheim, age_dx, time_t1d_yrs, gender, collard, fhist_pandz) %>% 
  tbl_summary(by = mheim, missing = "no",
              type = list(c(gender, collard, fhist_pandz, mheim) ~ "categorical"),
              label = list(gender ~ "Gender", 
                           fhist_pandz ~ "Family history of PD", 
                           age_dx ~ "Age at diagnosis", 
                           time_t1d_yrs ~ "Follow-up(years)")) %>% 
  add_p(test.args = variable_with_no_pval ~ list(workspace=2e9))

or

add_p(test.args = all_test("fisher.test") ~ list(workspace=2e9))