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.
John D. Murphy, MPH, PhD ORCID: 0000-0002-7714-9976
You can install the development version of riskdiff from GitHub with:
# install.packages("devtools")
devtools::install_github("jackmurphy2351/riskdiff")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# 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 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
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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
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#> 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# 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")The package uses generalized linear models with different link functions:
When the identity link fails to converge (common with binary outcomes), the package automatically tries alternative approaches.
# 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"# 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# 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
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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...
#> 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
#> Waiting for profiling to be done...
#> Waiting for profiling to be done...
#> Waiting for profiling to be done...
#> Warning: glm.fit: algorithm did not converge
#> Warning: step size truncated due to divergence
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#> Warning: glm.fit: algorithm did not converge
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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 identityThe 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 62Risk differences are particularly useful when:
| 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 |
browseVignettes("riskdiff")If you use this package in your research, please cite:
citation("riskdiff")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.