hermiteInterpolation

Cubic Hermite interpolation using both values and gradients to approximate a log likelihood function

Routines for combining causal effect estimates and study diagnostics across multiple data sites in a distributed study, without sharing patient-level data.
Allows for normal and non-normal approximations of the data-site likelihood of the effect parameter.

Martijn Schuemie

EvidenceSynthesis

Synthesizing Causal Evidence in a Distributed Research Network

Marc A. Suchard

Fan Bu

Observational Health Data Science and Informatics 

hermiteInterpolation function

<dl><dt>x</dt>
<dd>The log(hazard ratio) for which to approximate the log likelihood.</dd>
<dt>profile</dt>
<dd>A profile as created with <code>approximateLikelihood()</code> with
<code>approximation = "grid with gradients"</code>. This is a data frame
with 3 columns: <code>point</code>, <code>value</code>, and <code>derivative</code>, sorted by
<code>point</code>.</dd></dl>

Arguments

Cubic Hermite interpolation using both values and gradients to approximate a log likelihood function — hermiteInterpolation

<dl>

<dt>x</dt>
<dd>The log(hazard ratio) for which to approximate the log likelihood.</dd>


<dt>profile</dt>
<dd>A profile as created with <code>approximateLikelihood()</code> with
<code>approximation = "grid with gradients"</code>. This is a data frame
with 3 columns: <code>point</code>, <code>value</code>, and <code>derivative</code>, sorted by
<code>point</code>.</dd>

</dl>

hermiteInterpolation: Cubic Hermite interpolation using both values and gradients to approximate a log likelihood function

Description

Usage

Value

Arguments

Details

References

Examples