# Regression on imperfect class labels derived by unsupervised clustering

## TL;DR

This paper addresses bias in outcome regression models caused by misclassified class labels from unsupervised clustering, proposing a simulation and extrapolation method to correct for this bias, demonstrated through simulations and a real medical study.

## Contribution

It introduces a novel correction method for bias due to label misclassification in regression with unsupervised clustering-derived labels.

## Key findings

- The method reduces bias in simulated Gaussian mixture data.
- Application to multiple myeloma data shows improved survival analysis accuracy.

## Abstract

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.