iid.prodlim

Compute the influence function for each observation used to estimate the model

methods

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/worse/equivalently than a random observation drawn from the other group. Summary statistics such as the net treatment benefit, win ratio, or win odds are then deduced from these probabilities. 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

Eva Cantagallo

William Anderson

Julien Peron

Johan Verbeeck

iid.prodlim function

<dl><dt>x</dt>
<dd>A prodlim object.</dd>
<dt>add0</dt>
<dd>[logical] add the 0 to vector of relevant times.</dd>
<dt>...</dt>
<dd>not used. For compatibility with the generic method.</dd></dl>

Arguments

Author

Extract i.i.d. decomposition from a prodlim model — iid.prodlim

<dl>

<dt>x</dt>
<dd>A prodlim object.</dd>


<dt>add0</dt>
<dd>[logical] add the 0 to vector of relevant times.</dd>


<dt>...</dt>
<dd>not used. For compatibility with the generic method.</dd>

</dl>

iid.prodlim: Extract i.i.d. decomposition from a prodlim model

Description

Usage

Value

Arguments

Author

Details

References

Examples