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riskdiff

Lifecycle: experimental R-CMD-check Codecov test coverage CRAN Submission R-CMD-check Codecov test coverage

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.

Features

Author

John D. Murphy, MPH, PhD ORCID: 0000-0002-7714-9976

Installation

You can install the development version of riskdiff from GitHub with:

# install.packages("devtools")
devtools::install_github("jackmurphy2351/riskdiff")

Quick Start

library(riskdiff)

# Load example data
data(birthweight)

# Simple risk difference
result <- calc_risk_diff(
  data = birthweight,
  outcome = "low_birthweight",
  exposure = "smoking"
)
#> Waiting for profiling to be done...

print(result)
#> Risk Difference Analysis Results
#> ================================
#> 
#> Confidence level: 95% 
#> Number of comparisons: 1 
#> 
#>  Exposure Risk Difference          95% CI P-value    Model
#>   smoking           8.40% (5.21%, 11.97%)  <0.001 identity

Key Functions

Basic Usage

# Age-adjusted risk difference
rd_adjusted <- calc_risk_diff(
  data = birthweight,
  outcome = "low_birthweight", 
  exposure = "smoking",
  adjust_vars = "maternal_age"
)
#> Waiting for profiling to be done...

print(rd_adjusted)
#> Risk Difference Analysis Results
#> ================================
#> 
#> Confidence level: 95% 
#> Number of comparisons: 1 
#> 
#>  Exposure Risk Difference          95% CI P-value Model
#>   smoking           8.51% (2.77%, 14.25%)  <0.001   log

Stratified Analysis

# Stratified by race
rd_stratified <- calc_risk_diff(
  data = birthweight,
  outcome = "low_birthweight",
  exposure = "smoking",
  adjust_vars = "maternal_age",
  strata = "race"
)
#> Waiting for profiling to be done...
#> Warning: step size truncated due to divergence
#> Warning: step size truncated due to divergence
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#> Warning: glm.fit: algorithm did not converge
#> Warning: glm.fit: algorithm stopped at boundary value
#> Waiting for profiling to be done...
#> Waiting for profiling to be done...
#> Warning: step size truncated: out of bounds
#> Warning: step size truncated: out of bounds
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#> Warning: step size truncated: out of bounds
#> Warning: glm.fit: algorithm stopped at boundary value
#> Waiting for profiling to be done...

print(rd_stratified)
#> Risk Difference Analysis Results
#> ================================
#> 
#> Confidence level: 95% 
#> Number of comparisons: 4 
#> 
#>  Exposure Risk Difference           95% CI P-value    Model
#>   smoking          10.23%  (6.06%, 15.03%)  <0.001 identity
#>   smoking           8.05% (-5.25%, 21.34%)   0.024      log
#>   smoking           3.85%  (-1.38%, 9.08%)   0.211      log
#>   smoking          12.16% (-2.88%, 27.20%)   0.113 identity

Table Creation

# Create a simple text table
cat(create_simple_table(rd_stratified, "Risk by Smoking Status and Race"))
#> Risk by Smoking Status and Race
#> ====================================================================================
#> Exposure             Risk Diff       95% CI                    P-value    Model     
#> ====================================================================================
#> smoking              10.23%          (6.06%, 15.03%)           <0.001     identity  
#> smoking              8.05%           (-5.25%, 21.34%)          0.024      log       
#> smoking              3.85%           (-1.38%, 9.08%)           0.211      log       
#> smoking              12.16%          (-2.88%, 27.20%)          0.113      identity  
#> ====================================================================================
# Create publication-ready table (requires kableExtra)
library(kableExtra)
create_rd_table(rd_stratified, caption = "Risk of Low Birth Weight by Smoking Status")

Methodology

The package uses generalized linear models with different link functions:

  1. Identity link (preferred): Directly estimates risk differences
  2. Log link: Estimates relative risks, transforms to risk differences
  3. Logit link: Estimates odds ratios, transforms to risk differences

When the identity link fails to converge (common with binary outcomes), the package automatically tries alternative approaches.

Advanced Features

# Force a specific link function
rd_logit <- calc_risk_diff(
  birthweight, 
  "low_birthweight", 
  "smoking",
  link = "logit"
)
#> Waiting for profiling to be done...

# Check which model was used
rd_logit$model_type
#> [1] "logit"

Confidence Intervals

# 90% confidence intervals
rd_90 <- calc_risk_diff(
  birthweight,
  "low_birthweight", 
  "smoking",
  alpha = 0.10  # 1 - 0.10 = 90% CI
)
#> Waiting for profiling to be done...

print(rd_90)
#> Risk Difference Analysis Results
#> ================================
#> 
#> Confidence level: 90% 
#> Number of comparisons: 1 
#> 
#>  Exposure Risk Difference          95% CI P-value    Model
#>   smoking           8.40% (5.70%, 11.37%)  <0.001 identity

Multiple Stratification

# Stratify by multiple variables
rd_multi <- calc_risk_diff(
  data = birthweight,
  outcome = "low_birthweight",
  exposure = "smoking", 
  strata = c("race", "education")
)
#> Waiting for profiling to be done...
#> Warning: step size truncated due to divergence
#> Warning: step size truncated due to divergence
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#> Warning: glm.fit: algorithm did not converge
#> Warning: glm.fit: algorithm stopped at boundary value
#> Waiting for profiling to be done...
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#> Warning: step size truncated: out of bounds
#> Warning: glm.fit: algorithm stopped at boundary value
#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
#> Waiting for profiling to be done...
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#> Warning: glm.fit: algorithm did not converge
#> Warning: step size truncated due to divergence
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#> Warning: step size truncated due to divergence
#> Warning: glm.fit: algorithm did not converge
#> Waiting for profiling to be done...
#> Warning: step size truncated: out of bounds
#> Warning: step size truncated: out of bounds
#> Warning: step size truncated: out of bounds
#> Warning: step size truncated: out of bounds
#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
#> Waiting for profiling to be done...
#> Warning: step size truncated: out of bounds
#> Warning: glm.fit: algorithm stopped at boundary value
#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
#> Warning: step size truncated: out of bounds
#> Warning: glm.fit: algorithm stopped at boundary value
#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
#> Waiting for profiling to be done...
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# Show first few results
head(rd_multi)
#> Risk Difference Analysis Results
#> ================================
#> 
#> Confidence level: 95% 
#> Number of comparisons: 6 
#> 
#>  Exposure Risk Difference            95% CI P-value    Model
#>   smoking          -2.02% (-13.08%, 20.56%)   0.800 identity
#>   smoking          11.82%   (3.02%, 22.73%)   0.019 identity
#>   smoking          12.23%   (6.14%, 19.59%)  <0.001 identity
#>   smoking           6.59%   (0.06%, 16.14%)   0.106 identity
#>   smoking         -20.83% (-37.08%, -4.59%)   0.012 identity
#>   smoking          31.03%   (6.97%, 55.10%)   0.011 identity

Example Dataset

The package includes a simulated birth weight dataset with realistic associations:

data(birthweight)
str(birthweight)
#> 'data.frame':    2500 obs. of  8 variables:
#>  $ id             : int  1 2 3 4 5 6 7 8 9 10 ...
#>  $ smoking        : Factor w/ 2 levels "No","Yes": 1 1 1 1 1 1 1 1 2 1 ...
#>  $ maternal_age   : num  20 15 21 28 30 28 35 28 32 21 ...
#>  $ race           : Factor w/ 4 levels "White","Black",..: 1 1 1 1 1 1 1 2 3 1 ...
#>  $ education      : Factor w/ 4 levels "Less than HS",..: 3 4 4 4 2 2 2 2 3 2 ...
#>  $ prenatal_care  : Factor w/ 2 levels "Adequate","Inadequate": 1 1 1 1 1 1 1 2 1 1 ...
#>  $ parity         : num  3 1 2 0 0 1 3 0 0 2 ...
#>  $ low_birthweight: num  0 0 0 0 1 0 1 0 0 0 ...

# Summary statistics
table(birthweight$smoking, birthweight$low_birthweight)
#>      
#>          0    1
#>   No  1942  115
#>   Yes  381   62

When to Use Risk Differences

Risk differences are particularly useful when:

Comparison with Other Measures

Measure Interpretation Best When
Risk Difference Absolute change in risk Outcome common, policy decisions
Risk Ratio Relative change in risk Outcome rare, etiologic research
Odds Ratio Change in odds Case-control studies, rare outcomes

Getting Help

Citation

If you use this package in your research, please cite:

citation("riskdiff")

Code of Conduct

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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