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Bootstrap CI for Prevalence Ratios – Marginal Standardisation

Source: R/prLogisticBoot.R
prLogisticBootMarg.Rd

Estimates adjusted prevalence ratios (PR) using marginal standardisation (population-averaged) and obtains confidence intervals via bootstrap resampling.

Usage

prLogisticBootMarg(
  fit,
  data,
  conf = 0.95,
  R = 999L,
  ref_values = NULL,
  ref_continuous = c("median", "mean")
)

Arguments

fit

A fitted model object of class glm (binomial family), glmerMod (from lme4::glmer()), geeglm (from geepack::geeglm()), or svyglm (from survey::svyglm()). Must use the logit link.

data

Data frame used to fit fit. Required for bootstrapping.

conf

Numeric scalar in (0, 1): confidence level. Default 0.95.

R

Integer: number of bootstrap replicates. Default 999.

ref_values

Named list of reference values for specific predictors, e.g. list(age = 40, bmi = 25). Overrides automatic reference-value selection. For factor/dummy predictors the value should be 0 (the default) or 1.

ref_continuous

Character string: how to compute the reference value for continuous predictors when not supplied in ref_values. Either "median" (default) or "mean".

Value

An object of class "prLogistic" with components:

table

Numeric matrix with columns Estimate, lower and upper CI.

conf

Confidence level used.

method

"delta".

standardisation

"conditional" or "marginal".

model_type

Class of the fitted model.

call

The matched call.

Details

Marginal standardisation averages counterfactual predicted probabilities over the empirical covariate distribution, giving a population-averaged PR. At each bootstrap replicate the model is refitted and marginal PRs are recomputed.

See also

prLogisticDelta(), prLogisticBootCond()

Examples

fit_glm <- glm(case ~ induced + spontaneous + parity,
               family = binomial, data = infert)

set.seed(42)
res <- prLogisticBootMarg(fit_glm, data = infert, R = 199)
print(res)
#> 
#> Prevalence Ratio Estimation via Logistic Regression
#> ----------------------------------------------------
#>   Model        : glm 
#>   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%
#> induced       1.7024      1.1774       2.1367   1.3081    2.3990
#> spontaneous   3.0923      1.7405       4.1577   2.2437    4.7643
#> parity        0.8005      0.7306       0.8634   0.7422    0.8754
#>