evaluateCiCalibration: Evaluate confidence interval calibration

In EmpiricalCalibration: Routines for Performing Empirical Calibration of Observational Study Estimates

Evaluate confidence interval calibration

Description

evaluateCiCalibration performs a leave-one-out cross-validation to evaluate the calibration confidence intervals.

Usage

evaluateCiCalibration(
  logRr,
  seLogRr,
  trueLogRr,
  strata = as.factor(trueLogRr),
  crossValidationGroup = 1:length(logRr),
  legacy = FALSE
)

Arguments

logRr	A numeric vector of effect estimates on the log scale.
seLogRr	The standard error of the log of the effect estimates. Hint: often the standard error = (log(<lower bound 95 percent confidence interval>) - log(<effect estimate>))/qnorm(0.025).
trueLogRr	The true log relative risk.
strata	Variable used to stratify the plot. Set strata = NULL for no stratification.
crossValidationGroup	What should be the unit for the cross-validation? By default the unit is a single control, but a different grouping can be provided, for example linking a negative control to synthetic positive controls derived from that negative control.
legacy	If true, a legacy error model will be fitted, meaning standard deviation is linear on the log scale. If false, standard deviation is assumed to be simply linear.

Details

The empirical calibration is performed using a leave-one-out design: The confidence interval of an effect is computed by fitting a null using all other controls.

Value

A data frame specifying the coverage per strata (usually true effect size) for a wide range of widths of the confidence interval. The result also includes the fraction of estimates that was below and above the confidence interval.

Examples

## Not run: 
data <- simulateControls(n = 50 * 3, mean = 0.25, sd = 0.25, trueLogRr = log(c(1, 2, 4)))
eval <- evaluateCiCalibration(data$logRr, data$seLogRr, data$trueLogRr)

## End(Not run)

EmpiricalCalibration documentation built on Sept. 30, 2024, 9:12 a.m.