Vignette: Generating a Publication-Ready 'Table 1' with crosstable

# Create a sample data frame for a cancer trial
set.seed(123)
n_patients <- 200
cancer_trial <- data.frame(
  Age = rnorm(n_patients, mean = 65, sd = 10),
  Gender = factor(sample(c("Male", "Female"), n_patients, replace = TRUE)),
  Tumor_Grade = factor(sample(c("I", "II", "III"), n_patients, replace = TRUE, 
                              prob = c(0.3, 0.5, 0.2)), ordered = TRUE),
  Biomarker_X = rnorm(n_patients, mean = 100, sd = 25),
  Treatment_Group = factor(sample(c("Therapy", "Placebo"), n_patients, replace = TRUE))
)

# Ensure Age is numeric and integer
cancer_trial$Age <- as.integer(cancer_trial$Age)

head(cancer_trial)
##   Age Gender Tumor_Grade Biomarker_X Treatment_Group
## 1  59   Male          II    98.16110         Therapy
## 2  62   Male           I    70.78371         Placebo
## 3  80   Male          II    84.13129         Placebo
## 4  65   Male          II    99.27896         Therapy
## 5  66   Male          II   116.76740         Therapy
## 6  82   Male         III    58.73634         Therapy

# This code simulates how the jamovi module would be called in an R environment.
# You would need the ClinicoPathDescriptives package installed.

# Load the library
library(ClinicoPath)
## Registered S3 method overwritten by 'future':
##   method               from      
##   all.equal.connection parallelly
## Warning: replacing previous import 'dplyr::select' by 'jmvcore::select' when
## loading 'ClinicoPath'
## Warning: replacing previous import 'cutpointr::roc' by 'pROC::roc' when loading
## 'ClinicoPath'
## Warning: replacing previous import 'cutpointr::auc' by 'pROC::auc' when loading
## 'ClinicoPath'
## Warning: replacing previous import 'magrittr::extract' by 'tidyr::extract' when
## loading 'ClinicoPath'
## Warning in check_dep_version(): ABI version mismatch: 
## lme4 was built with Matrix ABI version 1
## Current Matrix ABI version is 0
## Please re-install lme4 from source or restore original 'Matrix' package
## Warning: replacing previous import 'jmvcore::select' by 'dplyr::select' when
## loading 'ClinicoPath'
## Registered S3 methods overwritten by 'ggpp':
##   method                  from   
##   heightDetails.titleGrob ggplot2
##   widthDetails.titleGrob  ggplot2
## Warning: replacing previous import 'DataExplorer::plot_histogram' by
## 'grafify::plot_histogram' when loading 'ClinicoPath'
## Warning: replacing previous import 'ROCR::plot' by 'graphics::plot' when
## loading 'ClinicoPath'
## Warning: replacing previous import 'dplyr::select' by 'jmvcore::select' when
## loading 'ClinicoPath'
## Warning: replacing previous import 'tibble::view' by 'summarytools::view' when
## loading 'ClinicoPath'

# Run the crosstable analysis with NEJM style
results <- crosstable(
    data = cancer_trial,
    vars = c("Age", "Gender", "Tumor_Grade", "Biomarker_X"),
    group = "Treatment_Group",
    sty = "nejm" # Specify the New England Journal of Medicine style
)

# The result is an HTML table, which would render directly in jamovi.
# In R, you would access it like this:
# results$tablestyle4

htmltools::HTML(results$tablestyle4$content)

Cross Table for Dependent treatment_group

	N	Placebo	Therapy	Test Statistic
		(N=93)	(N=107)
Age	200	58.0 64.0 71.0	58.0 64.0 70.0	F_1,198=0.01, P=0.92³
Gender : Male	200	0.5 42/93	0.5 51/107	Χ2 1=0.13, P=0.72²
Tumor_Grade	200			Χ2 2=0.88, P=0.65²
I		0.3 29/93	0.3 27/107
II		0.5 45/93	0.5 56/107
III		0.2 19/93	0.2 24/107
Biomarker_X	200	91.7 104.1 118.2	83.9 98.2 114.9	F_1,198=3.22, P=0.07³

N is the number of non-missing value. ¹Kruskal-Wallis. ²Pearson. ³Wilcoxon.

Analysis by Gemini

2025-06-30