Estimation of Prevalence Ratios via Logistic Regression Models

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 studies with common outcomes. Supports independent observations (glm()), clustered/multilevel data (glmer() from 'lme4'), longitudinal data via Generalised Estimating Equations (geeglm() from 'geepack'), and complex survey designs (svyglm() from 'survey'). Inference is available via the delta method (conditional and marginal standardisation) and via bootstrap (normal-approximation and percentile intervals). Continuous covariates are handled through user-specified or median-based reference values; flexible baseline specification allows any reference category to be chosen for factor predictors. Based on the methodology described in Amorim & Ospina (2021) .

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}
}