maicT2Test: Hotelling's T-square test to check whether maic is needed

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

Hotelling's T-square test to check whether maic is needed

Description

Hotelling's T-square test to check whether maic is needed

Usage

maicT2Test(ipd, ad, n.ad = Inf)

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.
n.ad	default is Inf assuming ad is a fixed (known) quantity with infinit accuracy. In most MAIC applications ad is the sample statistics and n.ad is known.

Details

When n.ad is not Inf, the covariance matrix is adjusted by the factor n.ad/(n.ipd + n.ad)), where n.ipd is nrow(ipd), the sample size of ipd.

Value

T.sq.f	the value of the T^2 test statistic
p.val	the p-value corresponding to the test statistic. When the p-value is small, matching is necessary.

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 the scenario A in the reference paper,
## i.e. when AD is perfectly within IPD
maicT2Test(eIPD, eAD[1,2:3])

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