maicWt function

Estimates the MAIC weights

Estimates the MAIC weights for each individual in the IPD. Should only be used after it is ascertained that AD is indeed within the convex hull of IPD.

maicWt(ipd, ad, max.it = 25)

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 coln. The matching variables should be in the same order as that in ipd. The function does not check this.
• max.it: maximum iteration passed to optim(). if ad is within ipd convex hull, then the default 25 iterations of optim() should be enough.

Returns

The main code are taken from Philippo (2016). It returns the following: - optim.out: results of optim()

• maic.wt: MAIC un-scaled weights for each subject in the IPD set

• maic.wt.rs: re-scaled weights which add up to the original total sample size, i.e. nrow(ipd)

• ipd.ess: effective sample size

• 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.

Examples

## eAD[1,] is scenario A in the reference manuscript
m1 <- maicWt(eIPD, eAD[1,2:3])

References

Phillippo DM, Ades AE, Dias S, et al. (2016). Methods for population-adjusted indirect comparisons in submissions to NICE. NICE Decision Support Unit Technical Support Document 18.