fitNull

<code>fitNull</code> fits the null distribution to a set of negative controls

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>.

Martijn Schuemie

EmpiricalCalibration

Routines for Performing Empirical Calibration of Observational
Study Estimates

Marc Suchard

fitNull function

<dl><dt>logRr</dt>
<dd>A numeric vector of effect estimates on the log scale</dd>
<dt>seLogRr</dt>
<dd>The standard error of the log of the effect estimates. Hint: often the standard
error = (log(&lt;lower bound 95 percent confidence interval&gt;) - log(&lt;effect
estimate&gt;))/qnorm(0.025)</dd></dl>

Arguments

Fit the null distribution — fitNull

<dl>

<dt>logRr</dt>
<dd>A numeric vector of effect estimates on the log scale</dd>


<dt>seLogRr</dt>
<dd>The standard error of the log of the effect estimates. Hint: often the standard
error = (log(&lt;lower bound 95 percent confidence interval&gt;) - log(&lt;effect
estimate&gt;))/qnorm(0.025)</dd>

</dl>

fitNull: Fit the null distribution

Description

Usage

Value

Arguments

Details

References

Examples