Feature Comparison

This article compares summata with other R packages for regression table generation and summary statistics.

Similar Packages

The R ecosystem includes several well-established packages for creating publication-ready regression tables. The following comparison identifies areas of overlap and distinction between summata and its alternatives.

Package	Primary Focus
summata	Fast regression workflows, forest plots, multivariate regression
gtsummary	Comprehensive table generation, `gt` ecosystem, maximum flexibility
finalfit	Clinical research, missing data handling, bootstrap simulations
arsenal	Large-scale summaries, SAS-like output
stargazer	Econometrics, LaTeX output, academic journal formatting
tableone	Simple Table 1 generation, SMD calculations
compareGroups	Bivariate analysis, clinical epidemiology

Core Feature Matrix

Feature	summata	gtsummary	finalfit	arsenal	stargazer	tableone	compareGroups
Descriptive Tables	✓	✓	✓	✓	✓	✓	✓
Stratified Summaries	✓	✓	✓	✓	—	✓	✓
Univariable Screening	✓	✓	✓	✓	—	—	✓
Multivariable Workflow	✓	✓	✓	✓	✓	—	✓
Multivariate Regression	✓	—	—	—	—	—	—
Model Comparison	✓	✓	◐	—	✓	—	—
Mixed-Effects Models	✓	✓	◐	—	◐	—	—
Cox/Survival Models	✓	✓	✓	✓	✓	—	✓
Interaction Formatting	✓	◐	◐	◐	—	—	—
Forest Plots	✓	◐	✓	—	—	—	—
Table Merge/Stack	—	✓	◐	—	—	—	—
Export (Word/PDF)	✓	✓	✓	✓	✓	◐	✓
Variable Labels	✓	✓	✓	✓	—	✓	✓

Legend: ✓ Full support | ◐ Partial support | — Not available

Feature Definitions

Feature	Description
Descriptive Tables	Summary statistics tables (mean, SD, median, IQR, n, %)
Stratified Summaries	Table 1-style summaries stratified by group with p-values
Univariable Screening	Test multiple predictors against one outcome (crude associations)
Multivariable Workflow	Combined univariable + multivariable analysis in one table
Multivariate Regression	Test one predictor across multiple outcomes
Model Comparison	Compare multiple models side-by-side with fit statistics
Mixed-Effects Models	Support for `lmer`/`glmer`/`coxme` random effects models
Cox/Survival Models	Cox proportional hazards and survival analysis
Interaction Formatting	Native support for interaction terms with formatted output
Forest Plots	Integrated forest plot visualization from regression results
Table Merge/Stack	Combine separate tables horizontally or vertically
Export (Word/PDF)	Direct export to publication formats
Variable Labels	Apply custom labels to variables in output

Unique Strengths of summata

The following features distinguish summata from comparable packages:

Multivariate Regression

The multifit() and multiforest() functions implement an inverted screening paradigm: testing a single predictor across multiple outcomes simultaneously. This workflow is common in epidemiological and clinical research but not directly supported by other packages.

# Test treatment effect across multiple outcomes
result <- multifit(
  data = clintrial,
  outcomes = c("surgery", "pfs_status", "os_status"),
  predictor = "treatment",
  covariates = c("age", "sex", "stage")
)

Integrated Forest Plots

Customizable forest plots are generated directly from analysis results without intermediate steps:

# From univariable screening
screen_result <- uniscreen(data, outcome, predictors)
uniforest(screen_result)

# From multivariate regression
multi_result <- multifit(data, outcomes, predictor)
multiforest(multi_result)

Performance Optimization

Built on data.table for computational efficiency, summata demonstrates 35–54% faster execution than comparable workflows in other packages with a smaller dependency footprint. See the Benchmarks article for detailed comparisons.

Unified API

All modeling functions share consistent syntax across model types:

# Same interface for different model types
fit(data, outcome, predictors, model_type = "glm")
fit(data, outcome, predictors, model_type = "coxph")
fit(data, outcome, predictors, model_type = "lmer", random = "(1|site)")

Complete Mixed-Effects Support

Full support for coxme (mixed-effects Cox models) alongside lmer and glmer, which is limited or absent in other packages.