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Summary Table with Optional Group Comparisons

Source: R/summary_table.R
summary_table.Rd

Generates a summary table for biostatistics and clinical data analysis with automatic normality, effect size, and statistical test calculations. Handles both numeric and categorical variables, performing appropriate descriptive statistics and inferential tests for single-group summaries or two-group comparisons.

Usage

summary_table(
  data,
  group_by = NULL,
  normality_test = "S-W",
  all = FALSE,
  effect_size = FALSE,
  exclude = NULL
)

Arguments

data

Dataframe containing the variables to be summarized.

group_by

Character string indicating the name of the grouping variable for two-group comparisons. Default: NULL.

normality_test

Character string indicating the normality test to use: 'S-W' for Shapiro-Wilk or 'K-S' for Kolmogorov-Smirnov with Lilliefors' correction. Default: 'S-W'.

all

Logical parameter that shows all calculated statistics. Default: FALSE.

effect_size

Logical parameter that includes effect size estimates. Default: FALSE.

exclude

Character vector of variable names to exclude from the summary. Default: NULL.

Value

A gt table object with formatted summary statistics.

Examples

# Simulated clinical data
clinical_df <- clinical_data()

# Overall summary without considering treatment groups
summary_table(clinical_df,
              exclude = c('participant_id', 'visit'))



  
variable
      n
      summary
      normality
    
sex
300
Male: 159 (53.0%); Female: 141 (47.0%)
NA
treatment
300
Placebo: 153 (51.0%); Treatment: 147 (49.0%)
NA
age
300
Median (IQR): 43.50 (20.50)
<0.001
weight
300
Median (IQR): 70.70 (21.52)
<0.001
biomarker
300
Mean (SD): 48.13 (9.70)
0.772
response
300
Complete: 88 (29.3%); Partial: 70 (23.3%); None: 142 (47.3%)
NA

# Grouped summary by treatment group
summary_table(clinical_df,
              group_by = 'treatment',
              exclude = c('participant_id', 'visit'))



  
variable
      n
      Placebo (Group A)
      Treatment (Group B)
      normality
      test
      p_value
    
sex
A:153, B:147
Male: 87 (56.9%); Female: 66 (43.1%)
Male: 72 (49.0%); Female: 75 (51.0%)
NA
Chi-squared
0.171
age
A:153, B:147
Median (IQR): 43.00 (17.00)
Median (IQR): 45.00 (27.00)
A:<0.001, B:<0.001
Mann-Whitney U
0.811
weight
A:153, B:147
Median (IQR): 67.60 (18.10)
Median (IQR): 74.90 (21.50)
A:<0.001, B:0.002
Mann-Whitney U
0.002
biomarker
A:153, B:147
Mean (SD): 48.96 (9.67)
Mean (SD): 47.26 (9.69)
A:0.588, B:0.694
Welch's t-test
0.131
response
A:153, B:147
Complete: 36 (23.5%); Partial: 36 (23.5%); None: 81 (52.9%)
Complete: 52 (35.4%); Partial: 34 (23.1%); None: 61 (41.5%)
NA
Chi-squared
0.059

# Grouped summary by treatment group with all stats and effect size
summary_table(clinical_df,
              group_by = 'treatment',
              all = TRUE,
              effect_size = TRUE,
              exclude = c('participant_id', 'visit'))



  
variable
      n
      Placebo (Group A)
      Treatment (Group B)
      normality
      test
      p_value
      effect_size
      effect_param
    
sex
A:153, B:147
Male: 87 (56.9%); Female: 66 (43.1%)
Male: 72 (49.0%); Female: 75 (51.0%)
NA
Chi-squared
0.171
0.08
Cramer's V
age
A:153, B:147
Mean (SD): 44.80 (13.7); Median (IQR): 43.00 (36.0,53.0); Range: 18.00,79.00
Mean (SD): 43.82 (16.2); Median (IQR): 45.00 (30.0,57.0); Range: 18.00,74.00
A:<0.001, B:<0.001
Mann-Whitney U
0.811
0.89
r
weight
A:153, B:147
Mean (SD): 70.70 (14.4); Median (IQR): 67.60 (61.7,79.8); Range: 45.00,105.70
Mean (SD): 75.12 (14.4); Median (IQR): 74.90 (65.7,87.2); Range: 45.00,102.60
A:<0.001, B:0.002
Mann-Whitney U
0.002
1.08
r
biomarker
A:153, B:147
Mean (SD): 48.96 (9.7); Median (IQR): 48.92 (42.0,55.7); Range: 21.96,70.24
Mean (SD): 47.26 (9.7); Median (IQR): 47.14 (40.8,53.2); Range: 24.07,75.67
A:0.588, B:0.694
Welch's t-test
0.131
0.17
Cohen's d
response
A:153, B:147
Complete: 36 (23.5%); Partial: 36 (23.5%); None: 81 (52.9%)
Complete: 52 (35.4%); Partial: 34 (23.1%); None: 61 (41.5%)
NA
Chi-squared
0.059
0.14
Cramer's V