MIML: Machine Learning Imputation, Clustering and Survival Analysis
for Longitudinal Proteomic Data
Imputes missing biomarker measurements in a wide longitudinal
serum panel with gradient-boosted decision trees, groups the completed
panel by Bayesian consensus clustering, and compares the resulting patient
subgroups by Kaplan-Meier, log-rank and Cox analysis. The imputation
learner is described in Ke et al. (2017)
<https://papers.nips.cc/paper/6907-lightgbm-a-highly-efficient-gradient-boosting-decision-tree>
and the clustering method in Lock and Dunson (2013)
<doi:10.1093/bioinformatics/btt425>. Imputed values are conditional-mean
predictions, so the procedure is a machine-learning single imputation; the
completions carry no between-imputation variance and must not be pooled by
Rubin's rules. Two panels from Gene Expression Omnibus accession
'GSE65622' are included, one for each survival endpoint.
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