testthat (>= 2.1.0), covr, osmar, RCurl, eixport, lwgeom,
rgdal

testthat (>= 2.1.0), covr, osmar, RCurl, lwgeom, rgdal

Routines for Performing Empirical Calibration of Observational
Study Estimates
Routines for performing empirical calibration of observational
study estimates. By using a set of negative control hypotheses we can
estimate the empirical null distribution of a particular observational
study setup. This empirical null distribution can be used to compute a
calibrated p-value, which reflects the probability of observing an
estimated effect size when the null hypothesis is true taking both random
and systematic error into account.

c(
person("Martijn", "Schuemie", , "schuemie@ohdsi.org", role = c("aut", "cre")),
person("Marc", "Suchard", role = c("aut"))
)
Routines for performing empirical calibration of observational
study estimates. By using a set of negative control hypotheses we can
estimate the empirical null distribution of a particular observational
study setup. This empirical null distribution can be used to compute a
calibrated p-value, which reflects the probability of observing an
estimated effect size when the null hypothesis is true taking both random
and systematic error into account. A similar approach can be used to
calibrate confidence intervals, using both negative and positive controls.
Martijn Schuemie [aut, cre],
Marc Suchard [aut]

c(
person("Martijn", "Schuemie", , "schuemie@ohdsi.org", role = c("aut", "cre"), comment = c(ORCID = "0000-0002-0817-5361")),
person("Marc", "Suchard", role = c("aut"), comment = c(ORCID = "0000-0001-9818-479X"))
)
Routines for performing empirical calibration of observational
study estimates. By using a set of negative control hypotheses we can
estimate the empirical null distribution of a particular observational
study setup. This empirical null distribution can be used to compute a
calibrated p-value, which reflects the probability of observing an
estimated effect size when the null hypothesis is true taking both random
and systematic error into account. A similar approach can be used to
calibrate confidence intervals, using both negative and positive controls.
For more details, see Schuemie et al. (2013) <doi:10.1002/sim.5925> and
Schuemie et al. (2018) <doi:10.1073/pnas.1708282114>.
ggplot2 (>= 2.0.0), gridExtra, methods, rlang
Martijn Schuemie [aut, cre] (<https://orcid.org/0000-0002-0817-5361>),
Marc Suchard [aut] (<https://orcid.org/0000-0001-9818-479X>)