Statistical Bioinformatics Webinar: Zhang -- Combining Machine Learning and Survival Analysis to Identify Recipient Sub-cohorts in a Heterogeneous Kidney Transplantation Population

Kidney transplant is the main remedy for end-stage renal disease and the prognosis of
allograft survival is what recipients care about the most.  A popular method for
allograft survival prediction in kidney transplantation is through the Cox proportional
hazard model.  There is a substantial literature and the performance of the published
models varies greatly.  One possible explanation driving this variability of performance
is the high heterogeneity that is intrinsic in the transplant population.  

We propose two complementary approaches (bottom-up and top-down) that aim to identify
recipient sub cohorts based on the inherent structure of the data which will improve
allograft survival.  The innovations of our approaches lie in combining supervised and
unsupervised learning, that is, it integrates machine learning methods with survival
analysis.  The bottom-up approach uses Numero, a new self-organising-map method, with
the elastic net Cox model to stratify potential recipient sub cohorts.  The alternative
top-down approach uses the Cox model with a contrast tree method to identify cohort
characteristics.  

Examining the results from both approaches, we find that recipient waiting time is an
important predictor in predicting graft survival for the whole population.  We also find
that there is a large amount of heterogeneity among ‘unfit’ recipients, these
recipients have sub cohorts that are particularly hard to predict in terms of their
graft survival.  In contrast, for younger and ‘fit’ cohorts, we found that
immunological factors are important components.  The ability to identify sub cohorts
based on prediction outcome is useful for enhancing prediction of graft survival and has
the potential guide allocation algorithm.