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Get A Summary Table From A cs_analysis Object

Source: R/cs_get_summary.R
summary_table.Rd

Retrieve the summary table in a tidy tibble format. This is especially useful to plot the results or conduct sensitivity analyses.

Usage

cs_get_summary(x, ...)

# Default S3 method
cs_get_summary(x, which = c("individual", "group"), ...)

# S3 method for class 'cs_anchor_group_within'
cs_get_summary(x, ...)

# S3 method for class 'cs_anchor_group_between'
cs_get_summary(x, ...)

Arguments

x

An object of class cs_analysis

...

Additional arguments passed to the respective method

which

Which level of summary table to return. This is only necessary for method "HA" since two summary tables are reported. Available are

  • individual, the default

  • group, group level results according to Hageman & Arrindell (1999)

Value

A tibble with clinical significance categories

References

  • Hageman, W. J., & Arrindell, W. A. (1999). Establishing clinically significant change: increment of precision and the distinction between individual and group level analysis. Behaviour Research and Therapy, 37(12), 1169–1193. https://doi.org/10.1016/S0005-7967(99)00032-7

See also

Extractor functions cs_get_augmented_data(), cs_get_data(), cs_get_model(), cs_get_n(), cs_get_reliability()

Examples

anchor_results <- claus_2020 |>
  cs_anchor(
    id,
    time,
    bdi,
    pre = 1,
    post = 4,
    mid_improvement = 8
  )

cs_get_summary(anchor_results)
#> # A tibble: 3 × 3
#>   category         n percent
#>   <fct>        <int>   <dbl>
#> 1 Improved        20   0.5  
#> 2 Unchanged       17   0.425
#> 3 Deteriorated     3   0.075


# Get summary table for a group level analysis
anchor_results_grouped <- claus_2020 |>
  cs_anchor(
    id,
    time,
    bdi,
    pre = 1,
    post = 4,
    mid_improvement = 8,
    target = "group"
  )

cs_get_summary(anchor_results_grouped)
#> # A tibble: 1 × 6
#>   difference lower upper    ci     n category                              
#>        <dbl> <dbl> <dbl> <dbl> <int> <chr>                                 
#> 1      -9.40 -12.8 -5.85  0.95    40 Probably clinically significant effect


# Get group-wise summary table for the Hageman & Arrindell method
combined_results <- claus_2020 |>
  cs_combined(
    id,
    time,
    bdi,
    pre = 1,
    post = 4,
    m_functional = 8,
    sd_functional = 8,
    reliability = 0.80,
    rci_method = "HA"
  )

cs_get_summary(combined_results)
#> $individual_level_summary
#> # A tibble: 5 × 3
#>   category         n percent
#>   <fct>        <int>   <dbl>
#> 1 Recovered        7   0.175
#> 2 Improved        18   0.45 
#> 3 Unchanged       15   0.375
#> 4 Deteriorated     0   0    
#> 5 Harmed           0   0    
#> 
#> $group_level_summary
#> # A tibble: 2 × 2
#>   category   percent
#>   <chr>        <dbl>
#> 1 Changed      0.841
#> 2 Functional   0.354
#> 
#> $categories
#> # A tibble: 40 × 12
#>       id   pre  post change cs_indiv clinical_post    rci recovered improved
#>    <dbl> <dbl> <dbl>  <dbl>    <dbl> <lgl>          <dbl> <lgl>     <lgl>   
#>  1     1    33    27     -6    1.72  FALSE         -1.50  FALSE     FALSE   
#>  2     2    26    19     -7   -0.393 FALSE         -1.67  FALSE     TRUE    
#>  3     3    15     5    -10   -4.08  FALSE         -2.18  FALSE     TRUE    
#>  4     5    39    46      7    6.73  FALSE          0.732 FALSE     FALSE   
#>  5     6    22    28      6    1.98  FALSE          0.561 FALSE     FALSE   
#>  6     7    25    18     -7   -0.657 FALSE         -1.67  FALSE     TRUE    
#>  7     8    33    30     -3    2.51  FALSE         -0.981 FALSE     FALSE   
#>  8     9    23     8    -15   -3.29  FALSE         -3.04  TRUE      FALSE   
#>  9    10    47    24    -23    0.925 FALSE         -4.41  FALSE     TRUE    
#> 10    11    43    13    -30   -1.97  FALSE         -5.61  TRUE      FALSE   
#> # ℹ 30 more rows
#> # ℹ 3 more variables: unchanged <lgl>, deteriorated <lgl>, harmed <lgl>
#> 
cs_get_summary(combined_results, which = "group")
#> $individual_level_summary
#> # A tibble: 5 × 3
#>   category         n percent
#>   <fct>        <int>   <dbl>
#> 1 Recovered        7   0.175
#> 2 Improved        18   0.45 
#> 3 Unchanged       15   0.375
#> 4 Deteriorated     0   0    
#> 5 Harmed           0   0    
#> 
#> $group_level_summary
#> # A tibble: 2 × 2
#>   category   percent
#>   <chr>        <dbl>
#> 1 Changed      0.841
#> 2 Functional   0.354
#> 
#> $categories
#> # A tibble: 40 × 12
#>       id   pre  post change cs_indiv clinical_post    rci recovered improved
#>    <dbl> <dbl> <dbl>  <dbl>    <dbl> <lgl>          <dbl> <lgl>     <lgl>   
#>  1     1    33    27     -6    1.72  FALSE         -1.50  FALSE     FALSE   
#>  2     2    26    19     -7   -0.393 FALSE         -1.67  FALSE     TRUE    
#>  3     3    15     5    -10   -4.08  FALSE         -2.18  FALSE     TRUE    
#>  4     5    39    46      7    6.73  FALSE          0.732 FALSE     FALSE   
#>  5     6    22    28      6    1.98  FALSE          0.561 FALSE     FALSE   
#>  6     7    25    18     -7   -0.657 FALSE         -1.67  FALSE     TRUE    
#>  7     8    33    30     -3    2.51  FALSE         -0.981 FALSE     FALSE   
#>  8     9    23     8    -15   -3.29  FALSE         -3.04  TRUE      FALSE   
#>  9    10    47    24    -23    0.925 FALSE         -4.41  FALSE     TRUE    
#> 10    11    43    13    -30   -1.97  FALSE         -5.61  TRUE      FALSE   
#> # ℹ 30 more rows
#> # ℹ 3 more variables: unchanged <lgl>, deteriorated <lgl>, harmed <lgl>
#>