676 – Analysis and Reporting: Benefit-Risk and Robust Models
Targeted Learning for Variable Importance in Precision Medicine
Yue You
University of California, Berkeley
Alan Hubbard
University of California, Berkeley
Rachael Callcut
University of California, San Fransisco
Lucy Kornblith
University of California, San Fransisco
Sabrinah Christie
University of California, San Fransisco
We proposed a parameter within a non-parametric model that measures the variable importance (VI) as the amount of attribution of that variable towards changes in the mean outcome. Specifically, for each of the candidate competing causes of the outcome, we utilized an estimate of this attribution using an approach based upon a combination of machine learning and causal inference via Targeted Learning. This approach allows for VI comparisons at the same scale, estimation not dependent on arbitrary parametric assumptions, and asymptotically linear (locally efficient) estimator where robust asymptotic inference is available. We implemented this approach to determine the VI of clinical measures in trauma patients in predicting the probability of mortality at different time periods from time of injury using data from three independent trauma cohorts. This approach allowed comparisons of VI within and between trauma cohorts and identified variables with the biggest potential "intervention" impact for mortality. Our results showed that the most important variables across all time intervals were mainly coagulation measures (such as initial International Normalized Ratio).