Estimating the impact of trauma treatment protocols is complicated by the
high dimensional yet finite sample nature of trauma data collected from
observational studies. Viscoelastic assays are highly predictive measures of
hemostasis. However, the effectiveness of thromboelastography(TEG) based
treatment protocols has not been statistically evaluated.To conduct robust and
reliable estimation with sparse data, we built an estimation "machine" for
estimating causal impacts of candidate variables using the collaborative
targeted maximum loss-based estimation(CTMLE) framework.The computational
efficiency is achieved by using the scalable version of CTMLE such that the
covariates are pre-ordered by summary statistics of their importance before
proceeding to the estimation steps.To extend the application of the estimator
in practice, we used super learning in combination with CTMLE to flexibly
choose the best convex combination of algorithms. By selecting the optimal
covariates set in high dimension and reducing constraints in choosing
pre-ordering algorithms, we are able to construct a robust and data-adaptive
model to estimate the parameter of interest.Under this estimation framework,
CTMLE outperformed the other doubly robust estimators(IPW,AIPW,stabilized
IPW,TMLE) in the simulation study. CTMLE demonstrated very accurate estimation
of the target parameter (ATE). Applying CTMLE on the real trauma data, the
treatment protocol (using TEG values immediately after injury) showed
significant improvement in trauma patient hemostasis status (control of
bleeding), and a decrease in mortality rate at 6h compared to standard care.The
estimation results did not show significant change in mortality rate at 24h
after arrival