boot.bias: Bootstrap resampling for selection and misclassification bias...

In episensr: Basic Sensitivity Analysis of Epidemiological Results

Bootstrap resampling for selection and misclassification bias models.

Description

Generate R bootstrap replicates of either selection or misclassification bias functions. It then generates a confidence interval of the parameter, by first order normal approximation or the bootstrap percentile interval. Replicates giving negative cell(s) in the adjusted 2-by-2 table are silently ignored.

Usage

boot.bias(bias_model, R = 1000, conf = 0.95, ci_type = c("norm", "perc"))

Arguments

bias_model	An object of class "episensr.boot", i.e. either selection bias function or misclassification bias function.
R	The number of bootstrap replicates.
conf	Confidence level.
ci_type	A character string giving the type of interval required. Values can be either "norm" or "perc", default to "norm".

Value

A list with elements:

model	Model ran.
boot_mod	Bootstrap resampled object, of class boot.
nrep	Number of replicates used.
bias_ciRR	Bootstrap confidence interval object for relative risk.
bias_ciOR	Bootstrap confidence interval object for odds ratio.
ci	Confidence intervals for the bias adjusted association measures.
conf	Confidence interval.

See Also

boot, selection, misclassification

Examples

misclass_eval <- misclassification(matrix(c(215, 1449, 668, 4296),
dimnames = list(c("Breast cancer+", "Breast cancer-"),
c("Smoker+", "Smoker-")),
nrow = 2, byrow = TRUE),
type = "exposure",
bias_parms = c(.78, .78, .99, .99))

set.seed(123)
boot.bias(misclass_eval)

episensr documentation built on Aug. 30, 2023, 5:09 p.m.