Abstract: Statistical techniques such as propensity score matching and instrumental variable are commonly employed to ``simulate" randomization and adjust for measured confounders in comparative effectiveness research. Despite such adjustments, the results of these methods apply essentially to an ``average" patient. However, as patients show significant heterogeneity in their responses to treatments, this average effect is of limited value. It does not account for individual level variabilities, which can deviate substantially from the population average.
To address this critical problem, we present a framework that allows the discovery of clinically meaningful homogeneous
subgroups with differential effects of plasma transfusion using unsupervised random forest clustering. Subgroup analysis using two blood transfusion datasets show that considerable variablilities exist between the subgroups and population in both the treatment effect of plasma transfusion on bleeding and mortality and risk factors for these outcomes. These results support the customization of blood transfusion therapy for the individual patient.
Learning Objective 1: After participating in this session, the learner should be better able to:
1) learn and understand the risk associated with plasma transfusion therapy and to answer the question “Is everyone at risk of plasma transfusion?”
2) Recognize the need for plasma transfusion to be customized for each individual patient.
3) Learn how to use unsupervised random forest for clustering and dimension reduction.
Che Ngufor (Presenter)
Matthew A. Warner, Mayco Clinic
Dennis Murphree, Mayo Clinic
Hongfang Liu, Mayo Clinic
Rickey Carter, Mayo Clinic
Curtis B. Storlie, Mayo Clinic
Daryl Kor, Mayo Clinic