Additional tests for gtsummary

See gtsummary::tests for more details on how defining custom tests. fisher.simulate.p() implements Fisher test with computation of p-values by Monte Carlo simulation in larger than 2×2 tables (see stats::fisher.test()). svyttest_oneway() is designed to compare means between sub-groups for survey objects. It is based on survey::svyttest() for comparing 2 means, and on svyoneway() for comparing 3 means or more.

Usage

fisher.simulate.p(data, variable, by, ...)

svyttest_oneway(data, variable, by, ...)

Arguments

data

A data set.

variable

Name of the variable to test.

by

Name of the by variable.

...

Unused.

Examples

library(gtsummary)
trial |>
  tbl_summary(include = grade, by = trt) |>
  add_p(test = all_categorical() ~ "fisher.simulate.p")


  
Characteristic
      
Drug A

N = 981
      
Drug B

N = 1021
      
p-value2
    
Grade




>0.9
    I
35 (36%)
33 (32%)

    II
32 (33%)
36 (35%)

    III
31 (32%)
33 (32%)

1 n (%)
    
2 Fisher’s Exact Test for Count Data with simulated p-value
(based on 2000 replicates)
    
# \donttest{
iris |>
  srvyr::as_survey() |>
  tbl_svysummary(
    include = Petal.Length,
    by = Species
   ) |>
   add_p(test = all_continuous() ~ svyttest_oneway)


  
Characteristic
      
setosa

N = 501
      
versicolor

N = 501
      
virginica

N = 501
      
p-value2
    
Petal.Length
1.50 (1.40, 1.60)
4.30 (4.00, 4.60)
5.50 (5.10, 5.90)
<0.001
1 Median (Q1, Q3)
    
2 Design-based one-way analysis of means
    
# }