A very common requirement for summary tables is to calculate the risk difference between treatment groups. add_risk_diff allows you to do this. The underlying risk difference calculations are performed using the Base R function prop.test - so prior to using this function, be sure to familiarize yourself with its functionality.

add_risk_diff(layer, ..., args = list(), distinct = TRUE)

## Arguments

layer

Layer upon which the risk difference will be attached

...

Comparison groups, provided as character vectors where the first group is the comparison, and the second is the reference

args

Arguments passed directly into prop.test

distinct

Logical - Use distinct counts (if available).

## Details

add_risk_diff can only be attached to a count layer, so the count layer must be constructed first. add_risk_diff allows you to compare the difference between treatment group, so all comparisons should be based upon the values within the specified treat_var in your tplyr_table object.

Comparisons are specified by providing two-element character vectors. You can provide as many of these groups as you want. You can also use groups that have been constructed using add_treat_grps or add_total_group. The first element provided will be considered the 'reference' group (i.e. the left side of the comparison), and the second group will be considered the 'comparison'. So if you'd like to see the risk difference of 'T1 - Placebo', you would specify this as c('T1', 'Placebo').

Tplyr forms your two-way table in the background, and then runs prop.test appropriately. Similar to way that the display of layers are specified, the exact values and format of how you'd like the risk difference display are set using set_format_strings. This controls both the values and the format of how the risk difference is displayed. Risk difference formats are set within set_format_strings by using the name 'riskdiff'.

You have 5 variables to choose from in your data presentation:

comp

Probability of the left hand side group (i.e. comparison)

ref

Probability of the right hand side group (i.e. reference)

dif

Difference of comparison - reference

low

Lower end of the confidence interval (default is 95%, override with the args paramter)

high

Upper end of the confidence interval (default is 95%, override with the args paramter)

Use these variable names when forming your f_str objects. The default presentation, if no string format is specified, will be:

f_str('xx.xxx (xx.xxx, xx.xxx)', dif, low, high)

Note - within Tplyr, you can account for negatives by allowing an extra space within your integer side settings. This will help with your alignment.

If columns are specified on a Tplyr table, risk difference comparisons still only take place between groups within the treat_var variable - but they are instead calculated treating the cols variables as by variables. Just like the tplyr layers themselves, the risk difference will then be transposed and display each risk difference as separate variables by each of the cols variables.

If distinct is TRUE (the default), all calculations will take place on the distinct counts, if they are available. Otherwise, non-distinct counts will be used.

One final note - prop.test may throw quite a few warnings. This is natural, because it alerts you when there's not enough data for the approximations to be correct. This may be unnerving coming from a SAS programming world, but this is R is trying to alert you that the values provided don't have enough data to truly be statistically accurate.

## Examples

library(magrittr)

## Two group comparisons with default options applied
t <- tplyr_table(mtcars, gear)

# Basic risk diff for two groups, using defaults
l1 <- group_count(t, carb) %>%
# Compare 3 vs. 4, 3 vs. 5
c('3', '4'),
c('3', '5')
)

# Build and show output
#> Warning: Chi-squared approximation may be incorrect
#> Warning: Chi-squared approximation may be incorrect
#> Warning: Chi-squared approximation may be incorrect
#> Warning: Chi-squared approximation may be incorrect
#> Warning: Chi-squared approximation may be incorrect
#> Warning: Chi-squared approximation may be incorrect
#> Warning: Chi-squared approximation may be incorrect
#> Warning: Chi-squared approximation may be incorrect
#> Warning: Chi-squared approximation may be incorrect
#> Warning: Chi-squared approximation may be incorrect
#> Warning: Chi-squared approximation may be incorrect
#> Warning: Chi-squared approximation may be incorrect
#> # A tibble: 6 × 8
#>   row_label1 var1_3     var1_4     var1_5    ord_layer_index rdiff_3_4 rdiff_3_5
#>   <chr>      <chr>      <chr>      <chr>               <int> <chr>     <chr>
#> 1 1          3 ( 20.0%) 4 ( 33.3%) 0 (  0.0…               1 "-0.133 … " 0.200 …
#> 2 2          4 ( 26.7%) 4 ( 33.3%) 2 ( 40.0…               1 "-0.067 … "-0.133 …
#> 3 3          3 ( 20.0%) 0 (  0.0%) 0 (  0.0…               1 " 0.200 … " 0.200 …
#> 4 4          5 ( 33.3%) 4 ( 33.3%) 1 ( 20.0…               1 " 0.000 … " 0.133 …
#> 5 6          0 (  0.0%) 0 (  0.0%) 1 ( 20.0…               1 " 0.000 … "-0.200 …
#> 6 8          0 (  0.0%) 0 (  0.0%) 1 ( 20.0…               1 " 0.000 … "-0.200 …
#> # … with 1 more variable: ord_layer_1 <dbl>

## Specify custom formats and display variables
t <- tplyr_table(mtcars, gear)

# Create the layer with custom formatting
l2 <- group_count(t, carb) %>%
# Compare 3 vs. 4, 3 vs. 5
c('3', '4'),
c('3', '5')
) %>%
set_format_strings(
'n_counts' = f_str('xx (xx.x)', n, pct),
'riskdiff' = f_str('xx.xxx, xx.xxx, xx.xxx, xx.xxx, xx.xxx', comp, ref, dif, low, high)
)

# Build and show output
#> Warning: Chi-squared approximation may be incorrect
#> Warning: Chi-squared approximation may be incorrect
#> Warning: Chi-squared approximation may be incorrect
#> Warning: Chi-squared approximation may be incorrect
#> Warning: Chi-squared approximation may be incorrect
#> Warning: Chi-squared approximation may be incorrect
#> Warning: Chi-squared approximation may be incorrect
#> Warning: Chi-squared approximation may be incorrect
#> Warning: Chi-squared approximation may be incorrect
#> Warning: Chi-squared approximation may be incorrect
#> Warning: Chi-squared approximation may be incorrect
#> Warning: Chi-squared approximation may be incorrect
#> # A tibble: 6 × 8
#>   row_label1 var1_3      var1_4      var1_5  ord_layer_index rdiff_3_4 rdiff_3_5
#>   <chr>      <chr>       <chr>       <chr>             <int> <chr>     <chr>
#> 1 1          " 3 (20.0)" " 4 (33.3)" " 0 ( …               1 " 0.200,… " 0.200,…
#> 2 2          " 4 (26.7)" " 4 (33.3)" " 2 (4…               1 " 0.267,… " 0.267,…
#> 3 3          " 3 (20.0)" " 0 ( 0.0)" " 0 ( …               1 " 0.200,… " 0.200,…
#> 4 4          " 5 (33.3)" " 4 (33.3)" " 1 (2…               1 " 0.333,… " 0.333,…
#> 5 6          " 0 ( 0.0)" " 0 ( 0.0)" " 1 (2…               1 " 0.000,… " 0.000,…
#> 6 8          " 0 ( 0.0)" " 0 ( 0.0)" " 1 (2…               1 " 0.000,… " 0.000,…
#> # … with 1 more variable: ord_layer_1 <dbl>

## Passing arguments to prop.test
t <- tplyr_table(mtcars, gear)

# Create the layer with args option
l3 <- group_count(t, carb) %>%
# Compare 3 vs. 4, 4 vs. 5
c('3', '4'),
c('3', '5'),
args = list(conf.level = 0.9, correct=FALSE, alternative='less')
)

# Build and show output
#> Warning: Chi-squared approximation may be incorrect
#> Warning: Chi-squared approximation may be incorrect
#> Warning: Chi-squared approximation may be incorrect
#> Warning: Chi-squared approximation may be incorrect
#> Warning: Chi-squared approximation may be incorrect
#> Warning: Chi-squared approximation may be incorrect
#> Warning: Chi-squared approximation may be incorrect
#> Warning: Chi-squared approximation may be incorrect
#> Warning: Chi-squared approximation may be incorrect
#> Warning: Chi-squared approximation may be incorrect
#> Warning: Chi-squared approximation may be incorrect
#> Warning: Chi-squared approximation may be incorrect
#> # A tibble: 6 × 8
#>   row_label1 var1_3     var1_4     var1_5    ord_layer_index rdiff_3_4 rdiff_3_5
#>   <chr>      <chr>      <chr>      <chr>               <int> <chr>     <chr>
#> 1 1          3 ( 20.0%) 4 ( 33.3%) 0 (  0.0…               1 "-0.133 … " 0.200 …
#> 2 2          4 ( 26.7%) 4 ( 33.3%) 2 ( 40.0…               1 "-0.067 … "-0.133 …
#> 3 3          3 ( 20.0%) 0 (  0.0%) 0 (  0.0…               1 " 0.200 … " 0.200 …
#> 4 4          5 ( 33.3%) 4 ( 33.3%) 1 ( 20.0…               1 " 0.000 … " 0.133 …
#> 5 6          0 (  0.0%) 0 (  0.0%) 1 ( 20.0…               1 " 0.000 … "-0.200 …
#> 6 8          0 (  0.0%) 0 (  0.0%) 1 ( 20.0…               1 " 0.000 … "-0.200 …
#> # … with 1 more variable: ord_layer_1 <dbl>