calibrateP computes calibrated p-values using the fitted null distribution
calibrateP(null, logRr, seLogRr, twoSided = TRUE, upper = TRUE, ...) # S3 method for null calibrateP(null, logRr, seLogRr, twoSided = TRUE, upper = TRUE, ...) # S3 method for mcmcNull calibrateP( null, logRr, seLogRr, twoSided = TRUE, upper = TRUE, pValueOnly, ... )
An object of class
null created using the
fitNull function or an
object of class
mcmcNull created using the
A numeric vector of one or more effect estimates on the log scale
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)
Compute two-sided (TRUE) or one-sided (FALSE) p-value?
If one-sided: compute p-value for upper (TRUE) or lower (FALSE) bound?
Any additional parameters (currently none).
If true, will return only the calibrated P-value itself, not the credible interval.
The calibrated p-value.
This function computes a calibrated two-sided p-value as described in Schuemie et al (2014).
null: Computes the calibrated P-value using asymptotic assumptions.
mcmcNull: Computes the calibrated P-value and 95 percent credible interval using Markov Chain
Monte Carlo (MCMC).
Schuemie MJ, Ryan PB, Dumouchel W, Suchard MA, Madigan D. Interpreting observational studies: why empirical calibration is needed to correct p-values. Statistics in Medicine 33(2):209-18,2014