Title:	Risk Difference Estimation with Multiple Link Functions and Inverse Probability of Treatment Weighting
Version:	0.2.1
Date:	2025-06-25
Description:	Calculates risk differences (or prevalence differences for cross-sectional data) using generalized linear models with automatic link function selection. Provides robust model fitting with fallback methods, support for stratification and adjustment variables, inverse probability of treatment weighting (IPTW) for causal inference, and publication-ready output formatting. Handles model convergence issues gracefully and provides confidence intervals using multiple approaches. Methods are based on approaches described in Mark W. Donoghoe and Ian C. Marschner (2018) "logbin: An R Package for Relative Risk Regression Using the Log-Binomial Model" <doi:10.18637/jss.v086.i09> for robust GLM fitting, Peter C. Austin (2011) "An Introduction to Propensity Score Methods for Reducing the Effects of Confounding in Observational Studies" <doi:10.1080/00273171.2011.568786> for IPTW methods, and standard epidemiological methods for risk difference estimation as described in Kenneth J. Rothman, Sander Greenland and Timothy L. Lash (2008, ISBN:9780781755641) "Modern Epidemiology".
License:	MIT + file LICENSE
Encoding:	UTF-8
RoxygenNote:	7.3.2
Depends:	R (≥ 3.5.0)
Imports:	dplyr (≥ 1.0.0), purrr, tibble, rlang, scales, stringr, stats, ggplot2
Suggests:	kableExtra, testthat (≥ 3.0.0), knitr, rmarkdown, covr
Config/testthat/edition:	3
URL:	https://github.com/jackmurphy2351/riskdiff
BugReports:	https://github.com/jackmurphy2351/riskdiff/issues
LazyData:	true
VignetteBuilder:	knitr
NeedsCompilation:	no
Packaged:	2025-06-25 17:27:08 UTC; johnmurphy
Author:	John D. Murphy [aut, cre] (MPH, PhD)
Maintainer:	John D. Murphy <jackmurphy2351@gmail.com>
Repository:	CRAN
Date/Publication:	2025-06-30 08:50:02 UTC

riskdiff: Risk Difference Estimation with Multiple Link Functions and Inverse Probability of Treatment Weighting

Description

Calculates risk differences (or prevalence differences for cross-sectional data) using generalized linear models with automatic link function selection. Provides robust model fitting with fallback methods, support for stratification and adjustment variables, inverse probability of treatment weighting (IPTW) for causal inference, and publication-ready output formatting. Handles model convergence issues gracefully and provides confidence intervals using multiple approaches. Methods are based on approaches described in Mark W. Donoghoe and Ian C. Marschner (2018) "logbin: An R Package for Relative Risk Regression Using the Log-Binomial Model" doi:10.18637/jss.v086.i09 for robust GLM fitting, Peter C. Austin (2011) "An Introduction to Propensity Score Methods for Reducing the Effects of Confounding in Observational Studies" doi:10.1080/00273171.2011.568786 for IPTW methods, and standard epidemiological methods for risk difference estimation as described in Kenneth J. Rothman, Sander Greenland and Timothy L. Lash (2008, ISBN:9780781755641) "Modern Epidemiology".

Author(s)

Maintainer: John D. Murphy jackmurphy2351@gmail.com (ORCID) (MPH, PhD)

See Also

Useful links:

• https://github.com/jackmurphy2351/riskdiff

• Report bugs at https://github.com/jackmurphy2351/riskdiff/issues

Assess Quality of Risk Difference Results

Description

Internal function to assess the quality and reliability of risk difference estimates based on multiple criteria including sample size, CI width, boundary issues, and model convergence.

Usage

.assess_result_quality(results)

Arguments

Value

Character vector of quality assessments

Detect Parameter Space Boundary Issues

Description

Detects when maximum likelihood estimates lie on or near the boundary of the parameter space for log-binomial and identity link models. Based on methods described in Donoghoe & Marschner (2018).

Usage

.detect_boundary(model, data, tolerance = 1e-06, verbose = FALSE)

Arguments

Value

A list containing:

boundary_detected

Logical indicating if boundary was detected

boundary_type

Character describing the type of boundary issue

boundary_parameters

Character vector of parameters on boundary

fitted_probabilities_range

Numeric vector with min/max fitted probabilities

separation_detected

Logical indicating complete/quasi-separation

References

Donoghoe MW, Marschner IC (2018). "logbin: An R Package for Relative Risk Regression Using the Log-Binomial Model." Journal of Statistical Software, 86(9), 1-22. doi:10.18637/jss.v086.i09

Detect Complete or Quasi-Separation

Description

Detects complete or quasi-separation in logistic-type models, which can cause boundary issues in parameter estimation.

Usage

.detect_separation(model, data, verbose = FALSE)

Arguments

Value

Logical indicating if separation was detected

Synthetic Cancer Risk Factor Study Data

Description

A synthetic dataset inspired by cancer screening and risk factor patterns observed during an opportunistic screening program conducted at the Cachar Cancer Hospital and Research Centre in Northeast India, specifically designed to reflect authentic epidemiological relationships without using real patient data.

Usage

cachar_sample

Format

A data frame with 2,500 rows and 12 variables:

id

Participant identifier (1 to 2500)

age

Age in years (continuous, range 18-84)

sex

Biological sex: "male" or "female"

residence

Residence type: "rural", "urban", or "urban slum"

smoking

Current smoking status: "No" or "Yes"

tobacco_chewing

Current tobacco chewing: "No" or "Yes"

areca_nut

Current areca nut use: "No" or "Yes"

alcohol

Current alcohol use: "No" or "Yes"

abnormal_screen

Binary outcome: 1 = abnormal screening (precancerous lesions or cancer), 0 = normal

head_neck_abnormal

Binary outcome: 1 = head/neck abnormality detected, 0 = normal

age_group

Age categories: "Under 40", "40-60", "Over 60"

tobacco_areca_both

Combined exposure: "Yes" if both tobacco_chewing and areca_nut are "Yes", "No" otherwise

Details

This synthetic dataset was designed to reflect authentic epidemiological patterns observed in Northeast India, particularly the distinctive tobacco and areca nut use patterns of the region. All data points are mathematically generated rather than collected from real individuals.

Key epidemiological features modeled:

• Areca nut use: Very high prevalence (~69%) reflecting regional cultural practices

• Tobacco chewing: Moderate to high prevalence (~53%), often used with areca nut

• Smoking: Lower prevalence (~13%) with strong male predominance

• Cancer outcomes: Realistic prevalence (~3.5%) for population-based screening, including both precancerous lesions and invasive cancers

• Geographic patterns: Predominantly rural population (~87%)

Synthetic Data Advantages: The synthetic approach preserves authentic statistical relationships while:

• Avoiding any privacy or ethical concerns

• Ensuring reproducible examples and tests

• Providing controlled demonstration scenarios

• Maintaining cultural authenticity for educational purposes

Risk Factor Relationships: The data models realistic dose-response relationships between multiple tobacco exposures and cancer outcomes, with particularly strong associations for areca nut use and head/neck abnormalities, reflecting authentic epidemiological patterns from this region.

Note

This synthetic dataset is designed for educational and software demonstration purposes. While the statistical relationships reflect authentic epidemiological patterns, the data should not be used for research conclusions about real populations. The cultural patterns represented (high areca nut use, specific tobacco consumption practices) are authentic to Northeast India.

Source

Synthetic dataset created for the riskdiff package. Inspired by cancer screening patterns observed in Northeast India but contains no real patient data. Statistical relationships designed to reflect authentic epidemiological patterns from this region for educational and methodological purposes.

References

Epidemiological patterns modeled after studies of tobacco use and cancer risk in Northeast India. For research involving actual populations from this region, consult published literature on areca nut and tobacco-related cancer risks in South Asian populations.

Warnakulasuriya S, Trivedy C, Peters TJ (2002). "Areca nut use: an independent risk factor for oral cancer." BMJ, 324(7341), 799-800.

Gupta PC, Ray CS (2004). "Epidemiology of betel quid use." Annals of the Academy of Medicine, Singapore, 33(4 Suppl), 31-36.

Examples

data(cachar_sample)
head(cachar_sample)

# Basic descriptive statistics
table(cachar_sample$areca_nut, cachar_sample$abnormal_screen)

# Regional tobacco use patterns
with(cachar_sample, table(areca_nut, tobacco_chewing))

# Simple risk difference for areca nut and abnormal screening
rd_areca <- calc_risk_diff(
  data = cachar_sample,
  outcome = "abnormal_screen",
  exposure = "areca_nut"
)
print(rd_areca)

# Age-adjusted analysis
rd_adjusted <- calc_risk_diff(
  data = cachar_sample,
  outcome = "abnormal_screen",
  exposure = "areca_nut",
  adjust_vars = "age"
)
print(rd_adjusted)

# Stratified by sex
rd_stratified <- calc_risk_diff(
  data = cachar_sample,
  outcome = "head_neck_abnormal",
  exposure = "smoking",
  strata = "sex"
)
print(rd_stratified)

# Multiple tobacco exposures comparison
rd_smoking <- calc_risk_diff(cachar_sample, "abnormal_screen", "smoking")
rd_chewing <- calc_risk_diff(cachar_sample, "abnormal_screen", "tobacco_chewing")
rd_areca <- calc_risk_diff(cachar_sample, "abnormal_screen", "areca_nut")

# Compare risk differences
cat("Risk differences for abnormal screening:\n")
cat("Smoking:", sprintf("%.1f%%", rd_smoking$rd * 100), "\n")
cat("Tobacco chewing:", sprintf("%.1f%%", rd_chewing$rd * 100), "\n")
cat("Areca nut:", sprintf("%.1f%%", rd_areca$rd * 100), "\n")

# Create summary table
cat(create_simple_table(rd_areca, "Abnormal Screening Risk by Areca Nut Use"))

Calculate Propensity Scores and IPTW Weights

Description

Calculates propensity scores and inverse probability of treatment weights for use in standardized risk difference estimation. Implements multiple approaches for weight calculation and includes diagnostic tools.

Usage

calc_iptw_weights(
  data,
  treatment,
  covariates,
  method = "logistic",
  weight_type = "ATE",
  stabilize = TRUE,
  trim_weights = TRUE,
  trim_quantiles = c(0.01, 0.99),
  verbose = FALSE
)

Arguments

Details

Propensity Score Estimation

The function fits a model predicting treatment assignment from covariates:

• Logistic regression: Standard approach, assumes logit link

• Probit regression: Uses probit link, may be more robust with extreme probabilities

• Complementary log-log: Useful when treatment is rare

Weight Types

• ATE weights: 1/pi(X) for treated, 1/(1-pi(X)) for controls

• ATT weights: 1 for treated, pi(X)/(1-pi(X)) for controls

• ATC weights: (1-pi(X))/pi(X) for treated, 1 for controls

Where pi(X) is the propensity score (probability of treatment given X).

Stabilized Weights

When stabilize=TRUE, weights are multiplied by marginal treatment probabilities to reduce variance while maintaining unbiasedness (Robins et al., 2000).

Weight Trimming

Extreme weights can cause instability. Trimming replaces weights outside specified quantiles with the quantile values (Crump et al., 2009).

Value

A list containing:

data

Original data with added propensity scores and weights

ps_model

Fitted propensity score model

weights

Vector of calculated weights

ps

Vector of propensity scores

diagnostics

List of diagnostic information including balance statistics

method

Method used for propensity score estimation

weight_type

Type of weights calculated

References

Austin PC (2011). "An Introduction to Propensity Score Methods for Reducing the Effects of Confounding in Observational Studies." Multivariate Behavioral Research, 46(3), 399-424. doi:10.1080/00273171.2011.568786

Crump RK, Hotz VJ, Imbens GW, Mitnik OA (2009). "Dealing with Limited Overlap in Estimation of Average Treatment Effects." Biometrika, 96(1), 187-199.

Hernan MA, Robins JM (2020). Causal Inference: What If. Boca Raton: Chapman & Hall/CRC.

Robins JM, Hernan MA, Brumback B (2000). "Marginal Structural Models and Causal Inference in Epidemiology." Epidemiology, 11(5), 550-560.

Examples

data(cachar_sample)

# Calculate ATE weights for areca nut use
iptw_result <- calc_iptw_weights(
  data = cachar_sample,
  treatment = "areca_nut",
  covariates = c("age", "sex", "residence", "smoking"),
  weight_type = "ATE"
)

# Check balance
print(iptw_result$diagnostics$balance_table)

# Calculate ATT weights (effect on the treated)
iptw_att <- calc_iptw_weights(
  data = cachar_sample,
  treatment = "tobacco_chewing",
  covariates = c("age", "sex", "residence", "areca_nut"),
  weight_type = "ATT"
)

Calculate Risk Differences with Robust Model Fitting and Boundary Detection

Description

Calculates risk differences (or prevalence differences for cross-sectional data) using generalized linear models with identity, log, or logit links. Version 0.2.1 includes enhanced boundary detection, robust confidence intervals, and improved data quality validation to prevent extreme confidence intervals in stratified analyses.

The function addresses common convergence issues with identity link binomial GLMs by implementing a fallback strategy across multiple link functions, similar to approaches described in Donoghoe & Marschner (2018) for relative risk regression.

Usage

calc_risk_diff(
  data,
  outcome,
  exposure,
  adjust_vars = NULL,
  strata = NULL,
  link = "auto",
  alpha = 0.05,
  boundary_method = "auto",
  verbose = FALSE
)

Arguments

Details

New in Version 0.2.1: Enhanced Stability and Quality Validation

This version adds comprehensive data quality validation to prevent the extreme confidence intervals that could occur in stratified analyses:

Enhanced Data Validation:

• Pre-analysis checks for stratification feasibility

• Detection of small sample sizes within strata

• Identification of rare outcomes or unbalanced exposures

• Warning for potential separation issues

Boundary Detection and Robust Inference:

When the MLE is on the boundary, standard asymptotic theory may not apply. The function detects and handles:

• upper_bound: Fitted probabilities approaching 1

• lower_bound: Fitted probabilities approaching 0

• separation: Complete or quasi-perfect separation

• both_bounds: Mixed boundary issues

Robust Confidence Intervals:

For boundary cases, implements:

• Profile likelihood intervals (preferred when feasible)

• Bootstrap confidence intervals (robust for complex cases)

• Modified Wald intervals with boundary adjustments

Risk Difference Interpretation

Risk differences represent absolute changes in probability. A risk difference of 0.05 means the exposed group has a 5 percentage point higher risk than the unexposed group. This is often more interpretable than relative measures (risk ratios, odds ratios) for public health decision-making.

Value

A tibble of class "riskdiff_result" containing the following columns:

exposure_var

Character. Name of exposure variable analyzed

rd

Numeric. Risk difference estimate (proportion scale, e.g. 0.05 = 5 percentage points)

ci_lower

Numeric. Lower bound of confidence interval

ci_upper

Numeric. Upper bound of confidence interval

p_value

Numeric. P-value for test of null hypothesis (risk difference = 0)

model_type

Character. Link function successfully used ("identity", "log", "logit", or error type)

n_obs

Integer. Number of observations used in analysis

on_boundary

Logical. TRUE if MLE is on parameter space boundary

boundary_type

Character. Type of boundary: "none", "upper_bound", "lower_bound", "separation", "both_bounds"

boundary_warning

Character. Warning message for boundary cases (if any)

ci_method

Character. Method used for confidence intervals ("wald", "profile", "bootstrap")

...

Additional columns for stratification variables if specified

The returned object has attributes including the original function call and alpha level used. Risk differences are on the probability scale where 0.05 represents a 5 percentage point difference.

References

Donoghoe MW, Marschner IC (2018). "logbin: An R Package for Relative Risk Regression Using the Log-Binomial Model." Journal of Statistical Software, 86(9), 1-22. doi:10.18637/jss.v086.i09

Marschner IC, Gillett AC (2012). "Relative Risk Regression: Reliable and Flexible Methods for Log-Binomial Models." Biostatistics, 13(1), 179-192.

Venzon DJ, Moolgavkar SH (1988). "A Method for Computing Profile-Likelihood-Based Confidence Intervals." Journal of the Royal Statistical Society, 37(1), 87-94.

Rothman KJ, Greenland S, Lash TL (2008). Modern Epidemiology, 3rd edition. Lippincott Williams & Wilkins.

Examples

# Simple risk difference
data(cachar_sample)
rd_simple <- calc_risk_diff(
  data = cachar_sample,
  outcome = "abnormal_screen",
  exposure = "areca_nut"
)
print(rd_simple)

# Age-adjusted risk difference
rd_adjusted <- calc_risk_diff(
  data = cachar_sample,
  outcome = "abnormal_screen",
  exposure = "areca_nut",
  adjust_vars = "age"
)
print(rd_adjusted)

# Stratified analysis with enhanced error checking and boundary detection
rd_stratified <- calc_risk_diff(
  data = cachar_sample,
  outcome = "abnormal_screen",
  exposure = "areca_nut",
  strata = "residence",
  verbose = TRUE  # See diagnostic messages and boundary detection
)
print(rd_stratified)

# Check for boundary cases
if (any(rd_stratified$on_boundary)) {
  cat("Boundary cases detected!\n")
  boundary_rows <- which(rd_stratified$on_boundary)
  for (i in boundary_rows) {
    cat("Row", i, ":", rd_stratified$boundary_type[i], "\n")
  }
}

# Force profile likelihood CIs for enhanced robustness
rd_profile <- calc_risk_diff(
  data = cachar_sample,
  outcome = "abnormal_screen",
  exposure = "areca_nut",
  boundary_method = "profile"
)

Calculate Standardized Risk Differences Using IPTW

Description

Calculates standardized risk differences using inverse probability of treatment weighting. This approach estimates causal effects under the assumption of no unmeasured confounding by creating a pseudo-population where treatment assignment is independent of measured confounders.

Usage

calc_risk_diff_iptw(
  data,
  outcome,
  treatment,
  covariates,
  iptw_weights = NULL,
  weight_type = "ATE",
  ps_method = "logistic",
  stabilize = TRUE,
  trim_weights = TRUE,
  alpha = 0.05,
  bootstrap_ci = FALSE,
  boot_n = 1000,
  verbose = FALSE
)

Arguments

Details

Causal Interpretation

IPTW estimates causal effects by weighting observations to create balance on measured confounders. The estimand depends on the weight type:

• ATE: Average treatment effect in the population

• ATT: Average treatment effect among those who received treatment

• ATC: Average treatment effect among those who did not receive treatment

Standard Errors

By default, uses robust (sandwich) standard errors that account for propensity score estimation uncertainty. Bootstrap confidence intervals are available as an alternative that may perform better with small samples.

Assumptions

1. No unmeasured confounding: All confounders are measured and included

2. Positivity: All subjects have non-zero probability of receiving either treatment

3. Correct model specification: Propensity score model is correctly specified

Value

A tibble of class "riskdiff_iptw_result" containing:

treatment_var

Character. Name of treatment variable

rd_iptw

Numeric. IPTW-standardized risk difference

ci_lower

Numeric. Lower confidence interval bound

ci_upper

Numeric. Upper confidence interval bound

p_value

Numeric. P-value for test of null hypothesis

weight_type

Character. Type of weights used

effective_n

Numeric. Effective sample size

risk_treated

Numeric. Risk in treated group

risk_control

Numeric. Risk in control group

Examples

data(cachar_sample)

# Standard ATE estimation
rd_iptw <- calc_risk_diff_iptw(
  data = cachar_sample,
  outcome = "abnormal_screen",
  treatment = "areca_nut",
  covariates = c("age", "sex", "residence", "smoking")
)
print(rd_iptw)

# ATT estimation with bootstrap CI
rd_att <- calc_risk_diff_iptw(
  data = cachar_sample,
  outcome = "head_neck_abnormal",
  treatment = "tobacco_chewing",
  covariates = c("age", "sex", "residence", "areca_nut"),
  weight_type = "ATT",
  bootstrap_ci = TRUE,
  boot_n = 500
)
print(rd_att)

Check IPTW Assumptions

Description

Provides diagnostic checks for key IPTW assumptions including positivity, balance, and model specification. Returns a comprehensive summary with recommendations for potential issues.

Usage

check_iptw_assumptions(
  iptw_result,
  balance_threshold = 0.1,
  extreme_weight_threshold = 10,
  verbose = TRUE
)

Arguments

Value

A list containing:

overall_assessment

Character indicating "PASS", "CAUTION", or "FAIL"

positivity

List with positivity checks and recommendations

balance

List with balance assessment and problematic variables

weights

List with weight distribution diagnostics

recommendations

Character vector of specific recommendations

Examples

data(cachar_sample)

iptw_result <- calc_iptw_weights(
  data = cachar_sample,
  treatment = "areca_nut",
  covariates = c("age", "sex", "residence", "smoking")
)

# Check assumptions
assumptions <- check_iptw_assumptions(iptw_result)
print(assumptions$overall_assessment)
print(assumptions$recommendations)

Create Balance Plots for IPTW Analysis

Description

Creates visualizations to assess covariate balance before and after IPTW weighting. Includes love plots (standardized differences) and propensity score distribution plots.

Usage

create_balance_plots(
  iptw_result,
  plot_type = "both",
  threshold = 0.1,
  save_plots = FALSE,
  plot_dir = "plots"
)

Arguments

Details

Love Plot

Shows standardized differences for each covariate before and after weighting. Points represent standardized differences, with lines connecting before/after values. Horizontal lines show common thresholds (0.1, 0.25) for acceptable balance.

Propensity Score Plot

Shows distributions of propensity scores by treatment group before and after weighting. Good overlap indicates positivity assumption is met.

Value

A ggplot object (if plot_type is "love" or "ps") or a list of ggplot objects (if plot_type is "both"). If ggplot2 is not available, returns a message and creates base R plots.

Examples

data(cachar_sample)

# Calculate IPTW weights
iptw_result <- calc_iptw_weights(
  data = cachar_sample,
  treatment = "areca_nut",
  covariates = c("age", "sex", "residence", "smoking")
)

# Create balance plots
if (requireNamespace("ggplot2", quietly = TRUE)) {
  plots <- create_balance_plots(iptw_result, plot_type = "both")
  print(plots$love_plot)
  print(plots$ps_plot)
}

Create Forest Plot for Risk Difference Results

Description

Creates a forest plot visualization of risk difference results, automatically detecting stratification variables and creating appropriate labels.

Usage

create_forest_plot(results, title = "Risk Differences", max_ci_width = 50, ...)

Arguments

Value

A ggplot object

Examples

data(cachar_sample)
results <- calc_risk_diff(cachar_sample, "abnormal_screen", "areca_nut", strata = "residence")
create_forest_plot(results)

Create Formatted Table of Risk Difference Results

Description

Creates a publication-ready table of risk difference results with appropriate grouping and formatting. Requires the kableExtra package for full functionality.

Usage

create_rd_table(
  results,
  caption = "Risk Differences",
  include_model_type = FALSE,
  ...
)

Arguments

Value

If kableExtra is available, returns a kable table object suitable for rendering in R Markdown or HTML. The table includes formatted risk differences, confidence intervals, and p-values with appropriate styling and footnotes. If kableExtra is not available, returns a formatted tibble with the same information in a basic data frame structure.

Examples

data(cachar_sample)
results <- calc_risk_diff(cachar_sample, "abnormal_screen", "smoking")

# Basic table (works without kableExtra)
basic_table <- create_rd_table(results, caption = "Risk of Abnormal Cancer Screening")
print(basic_table)

# Enhanced table (requires kableExtra)
if (requireNamespace("kableExtra", quietly = TRUE)) {
  enhanced_table <- create_rd_table(
    results,
    caption = "Risk of Abnormal Cancer Screening by Smoking Status",
    include_model_type = TRUE
  )
  print(enhanced_table)
}

Create a Simple Summary Table

Description

Creates a simple text-based summary table that doesn't require kableExtra.

Usage

create_simple_table(results, title = "Risk Difference Results")

Arguments

Value

A formatted character vector representing the table

Examples

data(cachar_sample)
results <- calc_risk_diff(cachar_sample, "abnormal_screen", "smoking")
cat(create_simple_table(results))

Create Summary Table for Risk Difference Results

Description

Creates a formatted summary table that works with any stratification variables.

Usage

create_summary_table(results, caption = "Risk Difference Results")

Arguments

Value

A data frame suitable for knitr::kable()

Format Risk Difference Results for Display

Description

Formats numerical values in risk difference results for presentation, with appropriate percentage formatting and rounding. Enhanced for v0.2.1 to handle boundary information and quality indicators with robust error handling.

Usage

format_risk_diff(
  results,
  digits = 2,
  p_accuracy = 0.001,
  show_ci_method = FALSE,
  show_quality = TRUE
)

Arguments

Value

Tibble with additional formatted columns including:

rd_formatted

Risk difference as formatted percentage string

ci_formatted

Confidence interval as formatted string

p_value_formatted

P-value with appropriate precision

quality_indicator

Quality assessment (if show_quality = TRUE)

ci_method_display

CI method information (if show_ci_method = TRUE)

Examples

data(cachar_sample)
results <- calc_risk_diff(cachar_sample, "abnormal_screen", "areca_nut")
formatted <- format_risk_diff(results)
print(formatted)

# Show CI methods and quality indicators
formatted_detailed <- format_risk_diff(results, show_ci_method = TRUE, show_quality = TRUE)
print(formatted_detailed)

# Customize formatting
formatted_custom <- format_risk_diff(results, digits = 3, p_accuracy = 0.01, show_quality = FALSE)
print(formatted_custom)

Get Quality Legend for Risk Difference Results

Description

Returns a legend explaining the quality indicators used in formatted results.

Usage

get_quality_legend()

Value

Character vector with quality indicator explanations

Examples

quality_legend <- get_quality_legend()
cat(paste(quality_legend, collapse = "\n"))

Get Valid Boundary Types

Description

Returns the complete list of valid boundary types that can be returned by the boundary detection function.

Usage

get_valid_boundary_types()

Value

Character vector of valid boundary type names

Print Method for IPTW Results

Description

Print Method for IPTW Results

Usage

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

Arguments

Print Method for IPTW Risk Difference Results

Description

Print Method for IPTW Risk Difference Results

Usage

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

Arguments

Print method for riskdiff_result objects

Description

Prints risk difference results in a formatted, readable way showing key statistics including risk differences, confidence intervals, model types used, and enhanced boundary case diagnostics for v0.2.1+.

Usage

## S3 method for class 'riskdiff_result'
print(x, show_boundary = TRUE, show_quality = TRUE, ...)

Arguments

Value

Invisibly returns the original riskdiff_result object (x). Called primarily for its side effect of printing formatted results to the console.

Examples

data(cachar_sample)
result <- calc_risk_diff(cachar_sample, "abnormal_screen", "areca_nut")
print(result)

# Suppress boundary details for cleaner output
print(result, show_boundary = FALSE)

Summary Method for IPTW Risk Difference Results

Description

Provides a comprehensive summary of IPTW risk difference analysis including effect estimates, diagnostics, and interpretation guidance.

Usage

## S3 method for class 'riskdiff_iptw_result'
summary(object, ...)

Arguments

Value

Invisibly returns the input object. Called primarily for side effects (printing summary).

Examples

data(cachar_sample)

rd_iptw <- calc_risk_diff_iptw(
  data = cachar_sample,
  outcome = "abnormal_screen",
  treatment = "areca_nut",
  covariates = c("age", "sex", "residence", "smoking")
)

summary(rd_iptw)