maxessWt: Maximum ESS Weights

In maicChecks: Exact Matching and Matching-Adjusted Indirect Comparison (MAIC)

Maximum ESS Weights

Description

Estimates an alternative set of weights which maximizes effective sample size (ESS) for a given set of variates used in the matching. Should only be used after it is ascertained that AD is indeed within the convex hull of IPD.

Usage

maxessWt(ipd, ad)

Arguments

ipd	a dataframe with n row and p column, where n is number of subjects and p is the number of variables used in matching.
ad	a dataframe with 1 row and p column. The matching variables should be in the same order as that in ipd. The function does not check this.

Details

The weights maximize the ESS subject to the set of baseline covariates used in the matching.

Value

maxess.wt	maximum ESS weights. Scaled to sum up to the total IPD sample size, i.e. nrow(ipd)
ipd.ess	effective sample size. It is no smaller than the ESS given by the MAIC weights.
ipd.wtsumm	weighted summary statistics of the matching variables after matching. they should be identical to the input AD when AD is within the IPD convex hull.

References

Glimm & Yau (2021). "Geometric approaches to assessing the numerical feasibility for conducting matching-adjusted indirect comparisons", Pharmaceutical Statistics, 21(5):974-987. doi:10.1002/pst.2210.

Examples

## eAD[1,] is scenario A in the reference manuscript
m0 <- maxessWt(eIPD, eAD[1,2:3])

maicChecks documentation built on April 3, 2025, 9:01 p.m.