README

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

Installation

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

# 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

@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}
}

prLogistic