  Curve matching is a prediction technique that relies on predictive mean
matching, which matches donors that are most similar to a target based on the
predictive distance. Even though this approach leads to high prediction
accuracy, the predictive distance may make matches look unconvincing, as the
profiles of the matched donors can substantially differ from the profile of the
target. To counterbalance this, similarity between the curves of the donors and
the target can be taken into account by combining the predictive distance with
the Mahalanobis distance into a `blended distance' measure. The properties of
this measure are evaluated in two simulation studies. Simulation study I
evaluates the performance of the blended distance under different
data-generating conditions. The results show that blending towards the
Mahalanobis distance leads to worse performance in terms of bias, coverage, and
predictive power. Simulation study II evaluates the blended metric in a setting
where a single value is imputed. The results show that a property of blending
is the bias-variance trade off. Giving more weight to the Mahalanobis distance
leads to less variance in the imputations, but less accuracy as well. The main
conclusion is that the high prediction accuracy achieved with the predictive
distance necessitates the variability in the profiles of donors.
