  Outcome regressed on class labels identified by unsupervised clustering is
custom in many applications. However, it is common to ignore the
misclassification of class labels caused by the learning algorithm, which
potentially leads to serious bias of the estimated effect parameters. Due to
its generality we suggest to redress the situation by use of the simulation and
extrapolation method. Performance is illustrated by simulated data from
Gaussian mixture models. Finally, we apply our method to a study which
regressed overall survival on class labels derived from unsupervised clustering
of gene expression data from bone marrow samples of multiple myeloma patients.
