Create Survey-Weighted Descriptive Statistics and Diagnostic Tables • svyTable1

svyTable1 is an R package for creating publication-ready tables and visualizations from complex survey and epidemiologic analyses. It streamlines the creation of descriptive “Table 1” summaries, supports multiply imputed regression models, provides diagnostic and goodness-of-fit tools, enables interaction effect reporting, and supports survey-weighted survival analysis.

✨ Key Features

Survey Design Integration: Built on svydesign objects from the survey package.
Automatic Missing Data Handling: Automatically reports missing values for each variable.
Best Practice Reporting: Defaults to “mixed mode” with unweighted Ns and weighted estimates.
Reliability Checks: Implements NCHS Data Presentation Standards for Proportions.
Flexible Output Modes: "mixed", "weighted", and "unweighted".
Survival Plotting: svykmplot() for publication-ready survey-weighted Kaplan Meier plots with number at risk.
Regression Diagnostics: svydiag() to evaluate coefficient reliability.
Goodness-of-Fit: svygof() to perform Archer-Lemeshow GOF tests for survey logistic regression.
Design-Correct AUC: svyAUC() for valid AUC estimation under complex survey designs.
Multiple Imputation Support: svypooled() generates fallacy-safe pooled regression tables from mice.
Interaction Analysis:
- Joint effects: jointeffects
- Simple effects: inteffects
- Additive interaction: addint, addintlist
Survey Cox Regression with Contrast Estimation:
svycoxph_CE() produces hazard ratio contrasts for groups or combinations.
MI-Compatible Cox Regression with Contrasts:
svycoxph_CE_mi() extends the above to multiply imputed datasets.
Plotting Interaction Effects:
plotint() generates publication-ready plots for interaction effects estimated with addint, jointeffects, or inteffects.
Automated Interaction Reporting:
reportint() creates narrative-ready summaries of interaction effects, RERI, AP, and joint effects.

🧩 Installation

# install.packages("devtools")
devtools::install_github("ehsanx/svyTable1", build_vignettes = TRUE, dependencies = TRUE)

🔍 Usage Examples (Selected)

Below are a few key examples. See vignettes for complete coverage.

Example: Interaction Reporting with reportint()

library(svyTable1)
library(survey)
library(dplyr)

data(NHANESraw)

analytic <- NHANESraw %>%
  filter(Age >= 20) %>%
  mutate(
    Obese = factor(ifelse(BMI >= 30, "Yes", "No")),
    Hypertension = factor(ifelse(BPSysAve >= 130, "Yes", "No"))
  ) %>%
  drop_na(Age, Race1, Obese, Hypertension, SDMVPSU, SDMVSTRA, WTMEC2YR)

design <- svydesign(
  id = ~SDMVPSU, strata = ~SDMVSTRA, weights = ~WTMEC2YR,
  nest = TRUE, data = analytic
)

model_int <- svyglm(
  Hypertension ~ Obese * Race1 + Age,
  design = design,
  family = quasibinomial()
)

reportint(model_int, factor1_name = "Obese", factor2_name = "Race1")

Example: Plotting Interaction Effects with plotint()

interaction_results <- addint(
  model = model_int,
  factor1_name = "Obese",
  factor2_name = "Race1",
  measures = "all"
)

plotint(interaction_results, measure = "RERI")

Example: Survey Cox Regression with Contrast Estimation

library(survival)

cox_model <- svycoxph(
  Surv(followup_time, death) ~ Obese + Age + Race1,
  design = design
)

svycoxph_CE(
  model = cox_model,
  contrast_var = "Obese",
  ref = "No",
  target = "Yes"
)

Example: Cox Contrasts with Multiple Imputation

library(mice)

imp <- mice(analytic, m = 5, seed = 123, printFlag = FALSE)

fit_list <- with(imp, {
  d <- svydesign(id = ~SDMVPSU, strata = ~SDMVSTRA, weights = ~WTMEC2YR,
                 nest = TRUE, data = complete(data))
  svycoxph(Surv(followup_time, death) ~ Obese + Age + Race1, design = d)
})

svycoxph_CE_mi(fit_list, contrast_var = "Obese", ref = "No", target = "Yes")

Example: Creating Interaction Tables

addintlist(
  model = model_int,
  factor1_name = "Obese",
  factor2_name = "Race1",
  measures = "all"
)

🤝 Contributing

Contributions are welcome.

📜 License

MIT License.

svyTable1: Create Publication-Ready Survey-Weighted Summary Tables