Bootstrap CI for Prevalence Ratios – Conditional Standardisation

Estimates adjusted prevalence ratios (PR) using conditional standardisation and obtains confidence intervals via bootstrap resampling (normal- approximation and percentile methods).

Usage

prLogisticBootCond(
  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

At each bootstrap replicate the model is refitted on a resampled dataset and conditional PRs are computed. Two CI types are returned:

Normal: Bootstrap normal-approximation interval.
Percentile: Empirical quantiles of the bootstrap distribution.

Use confint.prLogistic() with type = "normal" or type = "percentile" to extract a single CI type.

References

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

Davison, A. C. & Hinkley, D. V. (1997). Bootstrap Methods and their Application. Cambridge University Press.

Examples

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

set.seed(42)
res <- prLogisticBootCond(fit_glm, data = infert, R = 199)
#> Warning: Unusual PR estimate(s) detected for: spontaneous. Check model convergence and predictor coding.
#> Warning: Unusual PR estimate(s) detected for: spontaneous. Check model convergence and predictor coding.
#> Warning: Unusual PR estimate(s) detected for: spontaneous. Check model convergence and predictor coding.
#> Warning: Unusual PR estimate(s) detected for: spontaneous. Check model convergence and predictor coding.
#> Warning: Unusual PR estimate(s) detected for: spontaneous. Check model convergence and predictor coding.
print(res)
#> 
#> Prevalence Ratio Estimation via Logistic Regression
#> ----------------------------------------------------
#>   Model        : glm 
#>   Method       : bootstrap 
#>   Standardis.  : conditional 
#>   Conf. level  : 95% 
#> ----------------------------------------------------
#> 
#>                   Estimate    Normal CI    Percentile CI 
#>             Estimate Normal.2.5% Normal.97.5% Pct.2.5% Pct.97.5%
#> induced       2.5500      0.1918       4.1912   1.5021    5.5680
#> spontaneous   4.3606      0.1780       7.1674   2.5657   10.5113
#> parity        0.5941      0.4021       0.8071   0.3869    0.7931
#> 
plot(res)