SimtablR: Easy Publication-Ready Tables and Regression Analysis

Description

Streamlines the creation of descriptive frequency tables ('Table 1'), diagnostic test accuracy evaluations (sensitivity, specificity, predictive values), and multi-outcome regression summaries. Features automatic tables, prevalence and odds ratio calculations, and seamless integration with 'flextable' for exporting results to 'Microsoft Word' and 'PowerPoint'.

Author(s)

Maintainer: Matheus Trabuco Gonzalez matheustrabucogonzalez@gmail.com

See Also

Useful links:

• https://github.com/MatheusTG-14/SimtablR

• https://MatheusTG-14.github.io/SimtablR/

• Report bugs at https://github.com/MatheusTG-14/SimtablR/issues

Convert diag_test to Data Frame

Description

Extracts the performance metrics table as a plain data.frame.

Usage

## S3 method for class 'diag_test'
as.data.frame(x, ...)

Arguments

Value

A data.frame with columns Metric, Estimate, LowerCI, UpperCI.

Convert tb to Data Frame

Description

Convert tb to Data Frame

Usage

## S3 method for class 'tb'
as.data.frame(x, ...)

Arguments

Value

A data.frame with the formatted table.

Convert tb Object to Flextable

Description

Convert tb Object to Flextable

Usage

## S3 method for class 'tb'
as_flextable(x, ...)

Arguments

Value

A flextable object.

Diagnostic Test Accuracy Assessment

Description

Computes a 2x2 confusion matrix and comprehensive diagnostic performance metrics for a binary classification test, with exact binomial confidence intervals.

Usage

diag_test(
  data,
  test,
  ref,
  positive = NULL,
  test_positive = NULL,
  conf.level = 0.95
)

Arguments

Details

Confusion Matrix Layout

           | Ref +   | Ref -
-----------+---------+--------
Test +     |   TP    |   FP
Test -     |   FN    |   TN

Metrics Computed

• Sensitivity (Recall) = TP / (TP + FN)

• Specificity = TN / (TN + FP)

• PPV (Precision) = TP / (TP + FP)

• NPV = TN / (TN + FN)

• Accuracy = (TP + TN) / Total

• Prevalence = (TP + FN) / Total

• Likelihood Ratio + = Sensitivity / (1 - Specificity)

• Likelihood Ratio - = (1 - Sensitivity) / Specificity

• Youden's Index = Sensitivity + Specificity - 1

• F1 Score = 2 x (PPV x Sensitivity) / (PPV + Sensitivity)

Binomial CIs (exact Clopper-Pearson) are computed for the first six metrics. Likelihood Ratios, Youden's Index, and F1 Score do not have CIs.

Value

An object of class diag_test - a named list with:

• ⁠$table⁠: 2x2 table object (Test x Ref).

• ⁠$stats⁠: data.frame with columns Metric, Estimate, LowerCI, UpperCI.

• ⁠$labels⁠: named list with ref_pos, ref_neg, test_pos, test_neg.

• ⁠$sample_size⁠: integer, total valid observations.

• ⁠$conf.level⁠: numeric, confidence level used.

See Also

print.diag_test(), as.data.frame.diag_test(), plot.diag_test()

Examples

set.seed(1)
n   <- 200
ref <- factor(sample(c("No", "Yes"), n, replace = TRUE, prob = c(.55, .45)))
tst <- ifelse(ref == "Yes",
              ifelse(runif(n) < .80, "Yes", "No"),
              ifelse(runif(n) < .85, "No",  "Yes"))
df  <- data.frame(rapid_test = factor(tst), lab = ref)

result <- diag_test(df, test = rapid_test, ref = lab,
                    positive = "Yes", test_positive = "Yes")
print(result)
as.data.frame(result)

Simulated Epidemiological Dataset

Description

A simulated dataset containing demographic, clinical, and outcome variables for 500 individuals. Designed for demonstrating table creation and diagnostic testing analysis.

Usage

epitabl

Format

A data frame with 500 rows and 19 variables:

id

Unique patient identifier

age

Age in years (Numeric)

sex

Biological sex (Female, Male)

bmi

Body Mass Index in kg/m2 (Numeric, contains NAs)

smoking

Smoking status (Never, Former, Current)

exercise

Physical activity level (Low, Moderate, High)

education

Educational attainment (High School, Some College, College+)

income

Annual household income (<30k, 30-60k, 60k+)

disease

Disease status - primary outcome (No, Yes)

rapid_test

Result of rapid diagnostic test (Negative, Positive)

lab_confirmed

Laboratory confirmation - gold standard (No, Yes)

comorbidity_score

Score 0-5 based on medical history

outcome1

Count of primary care visits in past year

outcome2

Count of specialist visits in past year

outcome3

Count of emergency department visits in past year

hospitalized

Hospitalized in past year (No, Yes)

systolic_bp

Systolic blood pressure in mmHg

cholesterol

Total cholesterol in mg/dL

region

Geographic region (North, South, East, West)

Source

Simulated data for the SimtablR package.

Examples

data(epitabl)

# Basic description
tb(epitabl, sex, disease)

Export regtab Results to CSV

Description

Export regtab Results to CSV

Usage

export_regtab_csv(x, file, ...)

Arguments

Value

Invisibly returns x.

Export regtab Results to Excel

Description

Requires the openxlsx package.

Usage

export_regtab_xlsx(x, file, ...)

Arguments

Value

Invisibly returns x.

Plot Diagnostic Test Results

Description

Draws a fourfold display of the confusion matrix with sensitivity and specificity annotated on the bottom margin.

Usage

## S3 method for class 'diag_test'
plot(x, col = c("#ffcccc", "#ccffcc"), main = "Confusion Matrix", ...)

Arguments

Value

Invisibly returns x.

Print Method for diag_test Objects

Description

Displays a formatted summary of the confusion matrix and all diagnostic performance metrics with confidence intervals.

Usage

## S3 method for class 'diag_test'
print(x, digits = 3L, ...)

Arguments

Value

Invisibly returns x.

Print Method for regtab Results

Description

Print Method for regtab Results

Usage

## S3 method for class 'regtab'
print(x, ...)

Arguments

Value

Invisibly returns x.

Print Method for tb Objects

Description

Print Method for tb Objects

Usage

## S3 method for class 'tb'
print(x, digits = NULL, ...)

Arguments

Value

Invisibly returns x, called for side effects.

Multi-Outcome Regression Table

Description

Fits generalized linear models (GLMs) for multiple outcome variables and generates a formatted wide-format table with point estimates and confidence intervals. Supports robust standard errors, automatic exponentiation for count/binary outcomes, and custom labeling for publication-ready tables.

Usage

regtab(
  data,
  outcomes,
  predictors,
  family = poisson(link = "log"),
  robust = TRUE,
  exponentiate = NULL,
  labels = NULL,
  d = 2,
  conf.level = 0.95,
  include_intercept = FALSE,
  p_values = FALSE
)

Arguments

Details

Model Fitting

For each outcome, the function fits: glm(outcome ~ predictors, family = family, data = data)

Robust Standard Errors

When robust = TRUE, the function:

1. Fits the model with standard GLM.

2. Computes sandwich covariance matrix (HC0 estimator).

3. Calculates Wald-type CIs based on robust SEs.

This provides protection against heteroskedasticity and mild model misspecification.

Exponentiation

• Poisson regression: exp(beta) = Incidence Rate Ratio

  • IRR = 1: No association

  • IRR > 1: Increased rate

  • IRR < 1: Decreased rate

• Logistic regression: exp(beta) = Odds Ratio

  • OR = 1: No association

  • OR > 1: Increased odds

  • OR < 1: Decreased odds

Output Format

Returns a wide-format data.frame:

Variable    | Outcome1          | Outcome2          | ...
------------|-------------------|-------------------|----
(Intercept) | 2.34 (1.89-2.91) | 1.98 (1.65-2.38) | ...
age         | 1.05 (1.02-1.08) | 1.03 (1.01-1.06) | ...
sex         | 0.87 (0.75-1.01) | 0.92 (0.81-1.05) | ...

Each cell contains: "Estimate (Lower CI - Upper CI)"

Missing Data

GLM uses complete cases by default. Observations with missing values in any variable are excluded from that specific model.

Convergence Issues

If a model fails to converge or encounters errors:

• A warning is issued with the outcome name and error message

• That outcome column is skipped in the output

• Other outcomes continue processing

Value

A data.frame in wide format with:

• Variable: Predictor names (first column)

• Outcome columns: One column per outcome with formatted estimates and CIs

Can be directly exported to Excel, Word, or LaTeX for publication.

Examples

# Create example data
set.seed(456)
n <- 500
df <- data.frame(
  age = rnorm(n, 50, 10),
  sex = factor(sample(c("M", "F"), n, replace = TRUE)),
  treatment = factor(sample(c("A", "B"), n, replace = TRUE)),
  outcome1 = rpois(n, lambda = 5),
  outcome2 = rpois(n, lambda = 8),
  outcome3 = rpois(n, lambda = 3)
)

# Basic usage: Poisson regression for multiple outcomes
regtab(df,
       outcomes = c("outcome1", "outcome2", "outcome3"),
       predictors = ~ age + sex + treatment,
       family = poisson(link = "log"))

# With custom labels and no robust SEs
regtab(df,
       outcomes = c("outcome1", "outcome2"),
       predictors = "age + sex",
       labels = c(outcome1 = "Primary Endpoint", outcome2 = "Secondary Endpoint"),
       robust = FALSE)

# Logistic regression with p-values
df$binary_outcome <- rbinom(n, 1, 0.4)
regtab(df,
       outcomes = "binary_outcome",
       predictors = ~ age + sex,
       family = binomial(),
       p_values = TRUE)

Frequency and Summary Tables

Description

Creates comprehensive tables for categorical or continuous variables with formatting, statistical tests, prevalence ratios (PR), odds ratios (OR), and column stratification.

Usage

tb(
  data,
  ...,
  m = FALSE,
  d = 1,
  format = TRUE,
  style = "n_pct",
  style.rp = "{rp} ({lower} - {upper})",
  style.or = "{or} ({lower} - {upper})",
  test = FALSE,
  subset = NULL,
  strat = NULL,
  rp = FALSE,
  or = FALSE,
  ref = NULL,
  conf.level = 0.95,
  var.type = NULL,
  stat.cont = "median"
)

Arguments

Value

An object of class tb (a matrix with attributes).