Machine Learning Augmented Causal Inference To Estimate The Treatment Effect of Tranexamic Acid In Traumatic Brain Injury


Background: The CRASH-3 trial provides a high level of evidence on the question whether to administer Tranexamic Acid (TXA) for Traumatic brain injury (TBI). For numerous other research questions, the available evidence will not correspond to such a level of evidence and will rely on observational evidence only. The development of methodological alternatives to analyze observational data is necessary. The Crash-3trial provided the opportunity to explore the effect of TXA on TBI mortality with two distinct causal inference methods using incomplete observational data.
Methods: Two causal inference techniques, inverse propensity weighting (IPW) and doubly robust method (DR), associated with machine learning method techniques to handle missing data, explored the effect of TXA administration on 30-day head injury related death expressed in registry data. The effect was expressed as Average Treatment Effect (ATE). TBI was de ned as a head Abbreviated Injury Score> 2. The hypothesis expected the results to concur with the results obtained with the CRASH-3 benchmark trial.
Results: Between September 2010 and February 2019, from a total of 20037 registry cases 8269 corresponded to the definition of TBI. A total of 683 received TXA and 7565 did not. The observed head-injury related 30-day hospital mortality rate in the group TXA was 30%(205683) compared to 15% in the group no-TXA (11027565, p< 0.001). Causal inference with the IPW approach indicates an ATE with a higher mortality after TXA independently of the approach applied to manage missing data (ATE 0.10 (95% IC [0.06, 0.14]) or 0.09 (95% IC [0.03, 0.15])). ATE obtained with DR did not show any effect on mortality independently of the approach applied to missing data. No effect was observed in predefined subgroups.
Conclusions: This study demonstrated the feasibility to apply causal inference techniques in incomplete observational data. DR based on a stronger theoretical background compared to IPW, did not show a significant association of TXA administration with in-hospital mortality. This result provides a strong incentive to explore augmented causal inference techniques on incomplete observational data coupled with techniques to handle missing values.

Under review.
Imke Mayer
PhD, Research Scientist in Statistics and Causal Inference