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prLogistic

prLogistic estimates adjusted prevalence ratios (PR) and their confidence intervals from logistic regression models, addressing the well-known limitation of odds ratios (OR) as approximations to PR in cross-sectional and other studies with common binary outcomes.

Why PR instead of OR?

When the outcome is common (prevalence > 10%), the OR from logistic regression overestimates the PR. For example, with a 30% baseline prevalence and an OR = 2.5, the true PR is closer to 1.8. prLogistic corrects this using conditional or marginal standardisation directly on the logistic model.

Supported data structures

Function Model Use case
prLogisticDelta() glm Independent observations
prLogisticDelta() glmerMod Clustered / multilevel data
prLogisticGEE() geeglm Longitudinal data (GEE)
prLogisticSurvey() svyglm Complex survey designs

Installation 

# Stable release from CRAN
install.packages("prLogistic")

# Development version from GitHub
# install.packages("remotes")
remotes::install_github("Raydonal/prLogistic")

Quick start 

library(prLogistic)

# Fit a standard logistic model
data(birthwt, package = "MASS")
birthwt$smoke <- factor(birthwt$smoke)
birthwt$race  <- factor(birthwt$race)

fit <- glm(low ~ smoke + race + age + lwt,
           family = binomial, data = birthwt)

# --- Delta method --------------------------------------------------------

# Conditional PR (default: continuous covariates at median)
prLogisticDelta(fit, standardisation = "conditional")

# Marginal PR (population-averaged)
prLogisticDelta(fit, standardisation = "marginal")

# Custom reference: age = 25, lwt = 55 kg
prLogisticDelta(fit,
                standardisation = "conditional",
                ref_values = list(age = 25, lwt = 55))

# --- Bootstrap CIs -------------------------------------------------------
set.seed(42)
prLogisticBootCond(fit, data = birthwt, R = 999)

# --- Forest plot ---------------------------------------------------------
res <- prLogisticDelta(fit)
plot(res)

Longitudinal data (GEE) 

library(geepack)
data(ohio, package = "geepack")

fit_gee <- geeglm(resp ~ smoke + age,
                  family = binomial, id = id,
                  corstr = "exchangeable", data = ohio)

prLogisticGEE(fit_gee)

Complex survey data 

library(survey)
data(api, package = "survey")
dclus2 <- svydesign(id = ~dnum + snum, fpc = ~fpc1 + fpc2, data = apiclus2)
fit_svy <- svyglm(sch.wide ~ meals + stype,
                  design = dclus2, family = quasibinomial)

prLogisticSurvey(fit_svy)

Citation

If you use prLogistic in your research, please cite:

Amorim, L. D. & Ospina, R. (2021). Prevalence ratio estimation using R. Anais da Academia Brasileira de Ciências, 93(4), e20190316. https://doi.org/10.1590/0001-3765202120190316

@article{Amorim2021,
  author  = {Amorim, Leila D. and Ospina, Raydonal},
  title   = {Prevalence ratio estimation using {R}},
  journal = {Anais da Academia Brasileira de Ci\^{e}ncias},
  year    = {2021},
  volume  = {93},
  number  = {4},
  pages   = {e20190316},
  doi     = {10.1590/0001-3765202120190316}
}

Technical Documentation

License

GPL (≥ 2). See the GNU GPL v2 for details.