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.
