calcIntegralCif_cpp

[matrix] cif[1] = jump times in control group (event of interest), cif[2-3] = CIF of event of interest in group
T at times - tau and times + tau, cif[4] : jump in cif of control group at times (event of interest).

[numeric] Time at which to start the integral.

start_val

[numeric] Time at which to stop the integral.

stop_val

[numeric] CIF of event of interest in group T evaluated at observed time of treatment patient.

CIF_t

[numeric, &gt;0] last value of CIF of event type 1 in group T.

lastCIF

[numeric] Indicates the type of integral to compute (1 for wins, 2 for losses, 3 for neutral pairs with two
events of interest - integral with t+tau and xi - and 4 for neutral pairs with two events of interest - integral with
t+tau and t-tau).

type

Compute the integral with respect to the jump in CIF for pairs where both outcomes are censored.

internal

function

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 using permutations, a non-parametric bootstrap, or the asymptotic theory.
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.

calcIntegralCif_cpp: C++ Function Computing the Integral Terms for the Peron Method in the presence of competing risks (CR).

Description

Usage

Arguments