Prevalence Ratios for Longitudinal Data – GEE Models

A convenience wrapper around prLogisticDelta() for models fitted with geepack::geeglm(). GEE provides population-averaged (marginal) estimates suitable for longitudinal or clustered binary outcomes.

Usage

prLogisticGEE(
  fit,
  standardisation = c("marginal", "conditional"),
  conf = 0.95,
  method = c("delta", "bootstrap"),
  data = NULL,
  R = 999L,
  ref_values = NULL,
  ref_continuous = c("median", "mean")
)

Arguments

fit: A geeglm object fitted with family = binomial and link = "logit" (or family = binomial(link = "logit")).
standardisation: Character: "marginal" (default, recommended for GEE) or "conditional".
conf: Confidence level. Default 0.95.
method: Inference method: "delta" (default) or "bootstrap".
data: Data frame (required when method = "bootstrap").
R: Number of bootstrap replicates (only used when method = "bootstrap"). Default 999L.
ref_values: Named list of reference values. See prLogisticDelta().
ref_continuous: "median" (default) or "mean".

Value

A "prLogistic" object. See prLogisticDelta().

Details

GEE accounts for within-subject correlation through a working correlation structure (corstr argument of geeglm()). Common choices:

"independence": No correlation assumed (equivalent to GLM).
"exchangeable": Constant correlation across time points.
"ar1": First-order autoregressive; suitable for ordered time.
"unstructured": Estimates all pairwise correlations freely.

The robust (sandwich) variance-covariance matrix returned by vcov() on a geeglm object is used automatically, giving valid inference even when the working correlation structure is misspecified.

References

Zeger, S. L. & Liang, K.-Y. (1986). Longitudinal data analysis for discrete and continuous outcomes. Biometrics, 42, 121-130.

H?jsgaard, S., Halekoh, U. & Yan, J. (2006). The R package geepack for generalised estimating equations. Journal of Statistical Software, 15(2), 1-11.

Amorim, L. D. & Ospina, R. (2021). An Acad Bras Cienc, 93(4). doi:10.1590/0001-3765202120190316

Examples

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

# Model respiratory symptoms over time with exchangeable correlation
fit_gee <- geeglm(
  resp  ~ smoke + age,
  family = binomial,
  id     = id,
  corstr = "exchangeable",
  data   = ohio
)

# Marginal PR (recommended for GEE)
prLogisticGEE(fit_gee)
#> 
#> Prevalence Ratio Estimation via Logistic Regression
#> ----------------------------------------------------
#>   Model        : geeglm 
#>   Method       : delta 
#>   Standardis.  : marginal 
#>   Conf. level  : 95% 
#> ----------------------------------------------------
#> 
#>       Estimate   2.5%  97.5%
#> smoke   1.2499 0.9332 1.6742
#> age     0.9070 0.8410 0.9782
#> 

# With bootstrap CIs (small R for a fast example; use R >= 999 in practice)
prLogisticGEE(fit_gee, method = "bootstrap", data = ohio, R = 25)
#> Warning: extreme order statistics used as endpoints
#> Warning: extreme order statistics used as endpoints
#> 
#> Prevalence Ratio Estimation via Logistic Regression
#> ----------------------------------------------------
#>   Model        : geeglm 
#>   Method       : bootstrap 
#>   Standardis.  : marginal 
#>   Conf. level  : 95% 
#> ----------------------------------------------------
#> 
#>                   Estimate    Normal CI    Percentile CI 
#>       Estimate Normal.2.5% Normal.97.5% Pct.2.5% Pct.97.5%
#> smoke   1.2499      0.9657       1.4458   1.0794    1.5240
#> age     0.9070      0.8043       1.0188   0.8258    1.0302
#>