90 research outputs found
Personalized Treatment Selection via Product Partition Models with Covariates
Precision medicine is an approach for disease treatment that defines
treatment strategies based on the individual characteristics of the patients.
Motivated by an open problem in cancer genomics, we develop a novel model that
flexibly clusters patients with similar predictive characteristics and similar
treatment responses; this approach identifies, via predictive inference, which
one among a set of treatments is better suited for a new patient. The proposed
method is fully model-based, avoiding uncertainty underestimation attained when
treatment assignment is performed by adopting heuristic clustering procedures,
and belongs to the class of product partition models with covariates, here
extended to include the cohesion induced by the Normalized Generalized Gamma
process. The method performs particularly well in scenarios characterized by
considerable heterogeneity of the predictive covariates in simulation studies.
A cancer genomics case study illustrates the potential benefits in terms of
treatment response yielded by the proposed approach. Finally, being
model-based, the approach allows estimating clusters' specific response
probabilities and then identifying patients more likely to benefit from
personalized treatment.Comment: 31 pages, 7 figure
Model-based clustering of categorical data based on the Hamming distance
A model-based approach is developed for clustering categorical data with no
natural ordering. The proposed method exploits the Hamming distance to define a
family of probability mass functions to model the data. The elements of this
family are then considered as kernels of a finite mixture model with unknown
number of components. Conjugate Bayesian inference has been derived for the
parameters of the Hamming distribution model. The mixture is framed in a
Bayesian nonparametric setting and a transdimensional blocked Gibbs sampler is
developed to provide full Bayesian inference on the number of clusters, their
structure and the group-specific parameters, facilitating the computation with
respect to customary reversible jump algorithms. The proposed model encompasses
a parsimonious latent class model as a special case, when the number of
components is fixed. Model performances are assessed via a simulation study and
reference datasets, showing improvements in clustering recovery over existing
approaches
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