.information.logit

Compute the information (i.e. opposit of the expectation of the second derivative of the log-likelihood) for logistic regressions.

internal

Implementation of the Generalized Pairwise Comparisons (GPC)
as defined in Buyse (2010) <doi:10.1002/sim.3923> for complete observations,
and extended in Peron (2018) <doi:10.1177/0962280216658320> to deal with right-censoring.
GPC compare two groups of observations (intervention vs. control group)
regarding several prioritized endpoints to estimate the probability that a random observation drawn from
one group performs better than a random observation drawn from the other group (Mann-Whitney parameter).
The net benefit and win ratio statistics,
i.e. the difference and ratio between the probabilities relative to the intervention and control groups,
can then also be estimated. Confidence intervals and p-values are obtained based on asymptotic results (Ozenne 2021 <doi:10.1177/09622802211037067>),
non-parametric bootstrap, or permutations.
The software enables the use of thresholds of minimal importance difference,
stratification, non-prioritized endpoints (O Brien test), and can handle right-censoring and competing-risks.

Brice Ozenne

BuyseTest

Generalized Pairwise Comparisons

Julien Peron

Eva Cantagallo

.information.logit function

<dl><dt>object</dt>
<dd>a glm object corresponding to a logistic regression.</dd>
<dt>indiv</dt>
<dd>[logical] should the individual contribution be output instead of the total information?</dd>
<dt>center</dt>
<dd>[logical] should the individual contribution be centered around the average?</dd></dl>

Arguments

Information for Logistic Regressions — .information.logit

<dl>

<dt>object</dt>
<dd>a glm object corresponding to a logistic regression.</dd>


<dt>indiv</dt>
<dd>[logical] should the individual contribution be output instead of the total information?</dd>


<dt>center</dt>
<dd>[logical] should the individual contribution be centered around the average?</dd>

</dl>

.information.logit: Information for Logistic Regressions

Description

Usage

Arguments