Premium
Covariate dimension reduction for survival data via the Gaussian process latent variable model
Author(s) -
Barrett James E.,
Coolen Anthony C. C.
Publication year - 2015
Publication title -
statistics in medicine
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.996
H-Index - 183
eISSN - 1097-0258
pISSN - 0277-6715
DOI - 10.1002/sim.6784
Subject(s) - overfitting , covariate , dimensionality reduction , computer science , artificial intelligence , clustering high dimensional data , gaussian process , spurious relationship , latent variable , pattern recognition (psychology) , data mining , statistics , machine learning , gaussian , mathematics , cluster analysis , artificial neural network , physics , quantum mechanics
The analysis of high‐dimensional survival data is challenging, primarily owing to the problem of overfitting, which occurs when spurious relationships are inferred from data that subsequently fail to exist in test data. Here, we propose a novel method of extracting a low‐dimensional representation of covariates in survival data by combining the popular Gaussian process latent variable model with a Weibull proportional hazards model. The combined model offers a flexible non‐linear probabilistic method of detecting and extracting any intrinsic low‐dimensional structure from high‐dimensional data. By reducing the covariate dimension, we aim to diminish the risk of overfitting and increase the robustness and accuracy with which we infer relationships between covariates and survival outcomes. In addition, we can simultaneously combine information from multiple data sources by expressing multiple datasets in terms of the same low‐dimensional space. We present results from several simulation studies that illustrate a reduction in overfitting and an increase in predictive performance, as well as successful detection of intrinsic dimensionality. We provide evidence that it is advantageous to combine dimensionality reduction with survival outcomes rather than performing unsupervised dimensionality reduction on its own. Finally, we use our model to analyse experimental gene expression data and detect and extract a low‐dimensional representation that allows us to distinguish high‐risk and low‐risk groups with superior accuracy compared with doing regression on the original high‐dimensional data. Copyright © 2015 John Wiley & Sons, Ltd.