Distance-based outlier detection for high dimension, low sample size data Academic Article uri icon

Overview

MeSH Major

  • Light
  • Microscopy
  • Optical Tweezers

abstract

  • In clinical research, patient care decisions are often easier to make if patients are classified into a manageable number of groups based on homogeneous risk patterns. Investigators can use latent group-based trajectory modeling to estimate the posterior probabilities that an individual will be classified into a particular group of risk patterns. Although this method is increasingly used in clinical research, there is currently no measure that can be used to determine whether an individual's group assignment has a high level of discrimination. In this study, we propose a discrimination index and provide confidence intervals of the probability of the assigned group for each individual. We also propose a modified form of entropy to measure discrimination. The two proposed measures were applied to assess the group assignments of the longitudinal patterns of conduct disorders among young adolescent girls.

publication date

  • 2019

Research

keywords

  • Academic Article

Identity

Language

  • eng

PubMed Central ID

  • PMC4254619

Digital Object Identifier (DOI)

  • 10.1080/02664763.2018.1452901

PubMed ID

  • 25484482

Additional Document Info

start page

  • 1

end page

  • 11