Spring 2022

Causality Program Visitor Speaker Series: Causal Inference for Brain Trauma: Leveraging Incomplete Observational Data and RCT

Tuesday, Mar. 29, 2022 2:00 pm3:00 pm PDT

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Julie Josse (Inria)


Calvin Lab Room 116

The simultaneous availability of observational and experimental data for the same medical question about the effect of a treatment is an opportunity to combine their strengths and address their weaknesses. In this presentation, I will illustrate the methodological challenges we faced in answering a medical question about the effect of tranexamic acid administration on mortality in patients with traumatic brain injury in the context of critical care management. First, we had access to a large French observational registry on severely traumatized patients, but almost all variables were incomplete. We considered different sets of hypotheses under which causal inference is possible despite the missing attributes and discussed corresponding approaches to estimating the average treatment effect, including generalized propensity score methods and multiple imputation. Second, results from an international RCT were published that were not necessarily in agreement with those obtained from the observational study. This led us to investigate generalization problems where the trial data are considered a biased sample of a target population and we want to predict the treatment effect on the target population represented by the observational data. Identifying the treatment effect on the target population requires covariates in both sets that are treatment effect modifiers and that are shifted between the two sets. Standard estimators then use either weighting (IPSW) or outcome modeling (G-formula), or combine the two in doubly robust approaches (AIPSW). However, not all covariates needed for identification are often available in both sets. We have therefore computed the expected bias induced by a missing covariate and have proposed a sensitivity analysis.