Biometrical Journal

Clustering with missing and left‐censored data: A simulation study comparing multiple‐imputation‐based procedures

Early View

  • Author(s): Sylvie Chevret, Vassili Soumelis, Emmanuel Curis, Matthieu Resche‐Rigon, Lilith Faucheux
  • Article first published online: 06 Jul 2020
  • DOI: 10.1002/bimj.201900366
  • Read on Online Library
  • Subscribe to Journal

Abstract Cluster analysis, commonly used to explore large biomedical datasets, can be challenging, notably due to missing data or left‐censored data induced by the sensitivity limits of the biochemical measurement method. Usually, complete‐case analysis, simple imputation, or stochastic simple imputation are applied before clustering. More recently, consensus methods following multiple imputation have been proposed. However, they ignore left‐censoring and do not allow the number of clusters to vary across the partitions of each imputed dataset. Here, we developed a consensus‐based clustering algorithm in which left‐censored data are taken into account using a modified multiple imputation method and the number of clusters is estimated for each imputed dataset. A simulation study was conducted to assess the performance in terms of the number of clusters, the percentage of unclassified observations, and the adjusted Rand index. The simulation results showed that the investigated method works well compared to several alternative approaches. A real‐world application in breast cancer patients showed that the proposed method may reveal novel clusters of patients.

Related Topics

Related Publications

Related Content

Site Footer

Address:

This website is provided by John Wiley & Sons Limited, The Atrium, Southern Gate, Chichester, West Sussex PO19 8SQ (Company No: 00641132, VAT No: 376766987)

Published features on StatisticsViews.com are checked for statistical accuracy by a panel from the European Network for Business and Industrial Statistics (ENBIS)   to whom Wiley and StatisticsViews.com express their gratitude. This panel are: Ron Kenett, David Steinberg, Shirley Coleman, Irena Ograjenšek, Fabrizio Ruggeri, Rainer Göb, Philippe Castagliola, Xavier Tort-Martorell, Bart De Ketelaere, Antonio Pievatolo, Martina Vandebroek, Lance Mitchell, Gilbert Saporta, Helmut Waldl and Stelios Psarakis.