`R/EmpiricalCalibrationUsingAsymptotics.R`

, `R/EmpiricalCalibrationUsingMcmc.R`

`calibrateP.Rd`

`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,
...
)
```

- null
An object of class

`null`

created using the`fitNull`

function or an object of class`mcmcNull`

created using the`fitMcmcNull`

function.- logRr
A numeric vector of one or more 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)

- twoSided
Compute two-sided (TRUE) or one-sided (FALSE) p-value?

- upper
If one-sided: compute p-value for upper (TRUE) or lower (FALSE) bound?

- ...
Any additional parameters (currently none).

- pValueOnly
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