maicMD function

Checks if AD is within the convex hull of IPD using Mahalanobis distance

Should only be used when all matching variables are normally distributed

maicMD(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 only the sample statistics and n.ad is known.

Returns

Prints a message whether AD is furthest away from 0, i.e. IPD center in terms of Mahalanobis distance. Also returns ggplot object for plotting. - md.dplot: dot-plot of AD and IPD in Mahalanobis distance

• md.check: 0 = AD has the largest Mahalanobis distance to the IPD center; 2 = otherwise

Details

When AD does not have the largest Mahalanobis distance, in the original scale AD can still be outside of the IPD convex hull. On the other hand, when AD does have the largest Mahalanobis distance, in the original scale, AD is for sure outside the IPD convex hull.

Examples

## Not run:

## eAD[1,] is the scenario A in the reference paper,
## i.e. when AD is perfectly within IPD
md <- maicMD(eIPD, eAD[1,2:3])
md ## a dot-plot of IPD Mahalanobis distances along with AD in the same metric.
## End(Not run)

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.