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The riskdiff package provides robust methods for calculating risk differences (also known as prevalence differences in cross-sectional studies) using generalized linear models with automatic link function selection and boundary detection.
riskdiff v0.2.1 is now published on CRAN with cutting-edge boundary detection capabilities that identify when maximum likelihood estimates lie at the edge of the parameter space - a common issue with identity link models that other packages ignore.
John D. Murphy, MPH, PhD ORCID: 0000-0002-7714-9976
Install the latest stable version from CRAN:
install.packages("riskdiff")You can install the development version from GitHub with:
# install.packages("devtools")
devtools::install_github("jackmurphy2351/riskdiff")library(riskdiff)
# Load example data
data(cachar_sample)
# Simple risk difference with boundary detection
result <- calc_risk_diff(
data = cachar_sample,
outcome = "abnormal_screen",
exposure = "smoking"
)
#> Waiting for profiling to be done...
print(result)
#> Risk Difference Analysis Results (v0.2.1)
#> =========================================
#>
#> Confidence level: 95%
#> Number of comparisons: 1
#>
#> Exposure Risk Difference 95% CI P-value Model N Boundary
#> smoking 10.68% (5.95%, 15.75%) <0.001 identity 2500
#> Quality
#> Good# Create data that challenges standard GLM methods
set.seed(123)
challenging_data <- data.frame(
outcome = c(rep(0, 40), rep(1, 60)), # High baseline risk
exposure = factor(c(rep("No", 50), rep("Yes", 50))),
age = rnorm(100, 45, 10)
)
# riskdiff handles this gracefully with boundary detection
result <- calc_risk_diff(
data = challenging_data,
outcome = "outcome",
exposure = "exposure",
adjust_vars = "age",
verbose = TRUE # Shows diagnostic information
)
#> Warning in doTryCatch(return(expr), name, parentenv, handler): Possible
#> separation detected. Risk difference estimates may be unstable.
#> Formula: outcome ~ exposure + age
#> Sample size: 100
#> Trying identity link...
#> Using starting values: 0.2, 0.8, 0.004
#> Identity link error: cannot find valid starting values: please specify some
#> Trying log link...
#> log link error: no valid set of coefficients has been found: please supply starting values
#> Trying logit link...
#> Boundary detection results:
#> Boundary detected: TRUE
#> Boundary type: upper_boundary_near
#> Probability range: [0.17876, 1]
#> Note: Model converged but MLE is on parameter space boundary.
#> Boundary type: upper_boundary_near
#> ✓logit link converged
#> ⚠ Perfect or quasi-perfect separation detected. Results may be unreliable.
#> ⚠ Boundary case detected: upper_boundary_near
#> Using robust CI method: bootstrap
print(result)
#> Risk Difference Analysis Results (v0.2.1)
#> =========================================
#>
#> Confidence level: 95%
#> Number of comparisons: 1
#> Boundary cases detected: 1 of 1
#> Boundary CI method: auto
#>
#> Exposure Risk Difference 95% CI P-value Model N
#> exposure 80.06% (0.00%, 0.00%) — logit 100
#> Boundary Quality
#> ⚠ upper_boundary_near Boundary
#>
#> ⚠ Boundary Case Details:
#> =========================
#> Row 1 ( exposure ): Type: upper_boundary_near | CI method: bootstrap
#>
#> Boundary Type Guide:
#> • upper_bound: Fitted probabilities near 1 (risk saturation)
#> • lower_bound: Fitted probabilities near 0 (very rare outcomes)
#> • separation: Complete/quasi-separation detected
#> • both_bounds: Mixed boundary issues across observations
#> ⚠ indicates robust confidence intervals were used
#>
#> Statistical Note:
#> ================
#> Standard asymptotic theory may not apply for boundary cases.
#> Confidence intervals use robust methods when boundary detected.
#> For failed analyses, consider alternative estimation approaches.
# Check if boundary cases were detected
if (any(result$on_boundary)) {
cat("\n🚨 Boundary case detected! Using robust inference methods.\n")
cat("Boundary type:", unique(result$boundary_type[result$on_boundary]), "\n")
cat("CI method:", unique(result$ci_method[result$on_boundary]), "\n")
}
#>
#> 🚨 Boundary case detected! Using robust inference methods.
#> Boundary type: upper_boundary_near
#> CI method: bootstrap# Age-adjusted risk difference with boundary detection
rd_adjusted <- calc_risk_diff(
data = cachar_sample,
outcome = "abnormal_screen",
exposure = "smoking",
adjust_vars = "age",
boundary_method = "auto" # Automatic robust method selection
)
print(rd_adjusted)
#> Risk Difference Analysis Results (v0.2.1)
#> =========================================
#>
#> Confidence level: 95%
#> Number of comparisons: 1
#>
#> Exposure Risk Difference 95% CI P-value Model N Boundary Quality
#> smoking 10.94% (0.00%, 0.00%) — logit 2500 Good# Stratified by residence with boundary detection
rd_stratified <- calc_risk_diff(
data = cachar_sample,
outcome = "abnormal_screen",
exposure = "smoking",
adjust_vars = "age",
strata = "residence"
)
#> Waiting for profiling to be done...
print(rd_stratified)
#> Risk Difference Analysis Results (v0.2.1)
#> =========================================
#>
#> Confidence level: 95%
#> Number of comparisons: 3
#>
#> Exposure Risk Difference 95% CI P-value Model N Boundary
#> smoking 11.63% (0.00%, 0.00%) — logit 2158
#> smoking 9.99% (-5.89%, 25.87%) 0.218 identity 251
#> smoking -3.86% (0.00%, 0.00%) — log 91
#> Quality
#> Good
#> Wide CI
#> Good
# Summary of boundary cases across strata
boundary_summary <- rd_stratified[rd_stratified$on_boundary,
c("residence", "boundary_type", "ci_method")]
if (nrow(boundary_summary) > 0) {
cat("\nBoundary cases by stratum:\n")
print(boundary_summary)
}# Create a simple text table with boundary information
cat(create_simple_table(rd_stratified, "Risk by Smoking Status and Residence"))
#> Risk by Smoking Status and Residence
#> ====================================================================================
#> Exposure Risk Diff 95% CI P-value Model
#> ====================================================================================
#> smoking 11.63% (0.00%, 0.00%) NA logit
#> smoking 9.99% (-5.89%, 25.87%) 0.218 identity
#> smoking -3.86% (0.00%, 0.00%) NA log
#> ====================================================================================# Create publication-ready table (requires kableExtra)
library(kableExtra)
create_rd_table(rd_stratified,
caption = "Risk of Abnormal Screening Result by Smoking Status",
include_model_type = TRUE)The package uses generalized linear models with different link functions:
Key Innovation: When models hit parameter space
boundaries (common with identity links), the package: - 🔍
Detects boundary cases automatically - ⚠️ Warns
users about potential inference issues
- 🛡️ Uses robust confidence intervals when appropriate
- 📊 Reports methodology transparently
# Force specific boundary handling
rd_conservative <- calc_risk_diff(
cachar_sample,
"abnormal_screen",
"smoking",
boundary_method = "auto" # Options: "auto", "profile", "wald"
)
#> Waiting for profiling to be done...
# Check which methods were used
table(rd_conservative$ci_method)
#>
#> profile
#> 1# Force a specific link function
rd_logit <- calc_risk_diff(
cachar_sample,
"abnormal_screen",
"smoking",
link = "logit"
)
# Check which model was used and if boundaries detected
cat("Model used:", rd_logit$model_type, "\n")
#> Model used: logit
cat("Boundary detected:", rd_logit$on_boundary, "\n")
#> Boundary detected: FALSE# 90% confidence intervals with boundary detection
rd_90 <- calc_risk_diff(
cachar_sample,
"abnormal_screen",
"smoking",
alpha = 0.10 # 1 - 0.10 = 90% CI
)
#> Waiting for profiling to be done...
print(rd_90)
#> Risk Difference Analysis Results (v0.2.1)
#> =========================================
#>
#> Confidence level: 90%
#> Number of comparisons: 1
#>
#> Exposure Risk Difference 95% CI P-value Model N Boundary
#> smoking 10.68% (6.68%, 14.91%) <0.001 identity 2500
#> Quality
#> Good
# The package automatically uses appropriate CI methods for boundary cases# Examine the enhanced result structure
data(cachar_sample)
result <- calc_risk_diff(cachar_sample, "abnormal_screen", "smoking")
#> Waiting for profiling to be done...
names(result)
#> [1] "exposure_var" "rd" "ci_lower" "ci_upper"
#> [5] "p_value" "model_type" "on_boundary" "boundary_type"
#> [9] "boundary_warning" "ci_method" "n_obs"
# Key columns:
# - on_boundary: Was a boundary case detected?
# - boundary_type: What type of boundary?
# - boundary_warning: Detailed diagnostic message
# - ci_method: Which CI method was used?The package includes a realistic simulated cancer screening dataset:
data(cachar_sample)
str(cachar_sample)
#> 'data.frame': 2500 obs. of 12 variables:
#> $ id : int 1 2 3 4 5 6 7 8 9 10 ...
#> $ age : int 53 25 18 28 51 25 56 20 58 18 ...
#> $ sex : Factor w/ 2 levels "male","female": 2 1 2 2 1 2 1 1 1 1 ...
#> $ residence : Factor w/ 3 levels "rural","urban",..: 3 1 1 1 1 1 1 1 1 1 ...
#> $ smoking : Factor w/ 2 levels "No","Yes": 1 1 1 1 1 1 2 1 1 1 ...
#> $ tobacco_chewing : Factor w/ 2 levels "No","Yes": 2 1 1 2 2 1 2 1 2 2 ...
#> $ areca_nut : Factor w/ 2 levels "No","Yes": 2 2 2 2 1 1 2 1 2 2 ...
#> $ alcohol : Factor w/ 2 levels "No","Yes": 1 1 1 1 1 1 1 2 1 2 ...
#> $ abnormal_screen : int 0 0 0 0 0 0 1 0 1 0 ...
#> $ head_neck_abnormal: int 0 0 0 0 0 0 0 0 0 0 ...
#> $ age_group : Factor w/ 3 levels "Under 40","40-60",..: 2 1 1 1 2 1 2 1 2 1 ...
#> $ tobacco_areca_both: Factor w/ 2 levels "No","Yes": 2 1 1 2 1 1 2 1 2 2 ...
# Summary statistics showing realistic associations
table(cachar_sample$smoking, cachar_sample$abnormal_screen)
#>
#> 0 1
#> No 1851 317
#> Yes 248 84
# Risk difference analysis
rd_analysis <- calc_risk_diff(cachar_sample, "abnormal_screen", "smoking")
#> Waiting for profiling to be done...
cat("Smoking increases risk of abnormal screening result by",
sprintf("%.1f", rd_analysis$rd * 100), "percentage points\n")
#> Smoking increases risk of abnormal screening result by 10.7 percentage pointsRisk differences are particularly valuable when:
| Measure | Interpretation | Best When | riskdiff Advantage |
|---|---|---|---|
| Risk Difference | Absolute change in risk | Common outcomes, policy | Boundary detection |
| Risk Ratio | Relative change in risk | Rare outcomes | Standard methods only |
| Odds Ratio | Change in odds | Case-control studies | Standard methods only |
This package implements methods based on:
browseVignettes("riskdiff")If you use this package in your research, please cite:
citation("riskdiff")riskdiff uniquely provides boundary detection for robust inference!
Please note that the riskdiff project is released with a Contributor Code of Conduct. By contributing to this project, you agree to abide by its terms.
These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
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