Prevalence Ratios for Complex Survey Data

A convenience wrapper around prLogisticDelta() for logistic regression models fitted on complex survey data using survey::svyglm().

Usage

prLogisticSurvey(
  fit,
  standardisation = c("conditional", "marginal"),
  conf = 0.95,
  ref_values = NULL,
  ref_continuous = c("median", "mean")
)

Arguments

fit: A svyglm object fitted with family = quasibinomial (or binomial) and the logit link.
standardisation: Character: "conditional" (default) or "marginal".
conf: Confidence level. Default 0.95.
ref_values: Named list of reference values. See prLogisticDelta().
ref_continuous: "median" (default) or "mean".

Value

A "prLogistic" object. See prLogisticDelta().

Details

svyglm() incorporates sampling weights and complex design features (stratification, clustering, finite-population corrections) into parameter estimation. The design-consistent variance-covariance matrix is extracted automatically via vcov() and used in the delta-method calculations.

Note: bootstrap resampling for survey data requires design-aware resampling (e.g., survey bootstrap, balanced repeated replication). This is currently not automated; use prLogisticDelta() with a bootstrap-replicate survey design if needed.

References

Lumley, T. (2004). Analysis of complex survey samples. Journal of Statistical Software, 9(1), 1-19.

Lumley, T. (2010). Complex Surveys: A Guide to Analysis Using R. Wiley, New Jersey.

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

Examples

library(survey)
data(api, package = "survey")

# Create binary outcome
apiclus2$target_met <- as.numeric(apiclus2$sch.wide == "Yes")

# Stratified two-stage cluster sample
dclus2 <- svydesign(
  id   = ~dnum + snum,
  fpc  = ~fpc1 + fpc2,
  data = apiclus2
)

fit_svy <- svyglm(
  target_met ~ meals + stype,
  design = dclus2,
  family = quasibinomial
)

prLogisticSurvey(fit_svy, standardisation = "conditional")
#> 
#> Prevalence Ratio Estimation via Logistic Regression
#> ----------------------------------------------------
#>   Model        : svyglm 
#>   Method       : delta 
#>   Standardis.  : conditional 
#>   Conf. level  : 95% 
#> ----------------------------------------------------
#> 
#>        Estimate   2.5%  97.5%
#> meals    0.9996 0.9989 1.0003
#> stypeH   0.1491 0.0505 0.4403
#> stypeM   0.6616 0.4246 1.0310
#> 
prLogisticSurvey(fit_svy, standardisation = "marginal")
#> 
#> Prevalence Ratio Estimation via Logistic Regression
#> ----------------------------------------------------
#>   Model        : svyglm 
#>   Method       : delta 
#>   Standardis.  : marginal 
#>   Conf. level  : 95% 
#> ----------------------------------------------------
#> 
#>        Estimate   2.5%  97.5%
#> meals    0.9982 0.9959 1.0005
#> stypeH   0.1341 0.0367 0.4899
#> stypeM   0.6078 0.2681 1.3777
#>