CMIPS Seminar: Judith Lok "Causal mediation analysis and the promise of alpha-DEspR, a preclinical potential COVID-19 treatment in the ICU"

Bio: Judith Lok is a Professor of Mathematics and Statistics at Boston University. She has been on the faculty of the Department of Mathematics and Statistics at Boston University since 2018. She does research on adaptive clinical trial designs (LAGO), causal inference, and survival analysis. Her application areas include HIV, COVID-19 in the ICU, bacterial infections, HCV, overdoses, and mother and child health. She obtained her PhD in Mathematical Statistics from the Free University of Amsterdam in 2001. Before her PhD, she worked as an MS statistician at the Amsterdam Medical Center, and after her PhD, she was a postdoc in mathematical statistics (Utrecht) and then medical statistics (Leiden), all in The Netherlands. In 2005 she became a visiting scholar at the Departments of Epidemiology and Biostatistics of the Harvard TH Chan School of Public Health, and in 2006 an Assistant Professor and then an Associate Professor of Biostatistics at the Harvard TH Chan School of Public Health. At Boston University she teaches a causal inference course that she proposed and developed, for undergraduates, MS students, and PhD students, and she also teaches other statistics courses. Judith Lok is MPI on and R01 on LAGO with Donna Spiegelman.

Abstract: Mediation analysis, which started in the mid-1980s, is used extensively by applied researchers. Indirect and direct effects are the parts of a treatment effect that is mediated by a covariate (indirect effect) and the part that is not (direct effect). Subsequent work on natural and pure indirect and direct effects provides a formal causal interpretation, based on cross-worlds counterfactuals: outcomes under treatment with the mediator set to its value without treatment. Organic indirect and direct effects avoid cross-worlds counterfactuals, using so-called organic interventions on the mediator while keeping the initial treatment fixed. We argue that pure and organic indirect effects are very relevant for drug development. 1] They are often the effect of a treatment through its intended pathway, and 2] they can be estimated without on-treatment outcome data. We illustrate our approach by estimating the pure/organic indirect effect of alpha-DEspR, a potential treatment for COVID-19 in the ICU, mediated by DEspR+ neutrophil nets. alpha-DEspR targets elimination of DEspR+[NET+Ns] to attenuate or prevent multi-organ failure in critical COVID-19. alpha-DEspR eliminates DEspR+ neutrophil nets in rats and in petri dishes; it is hoped, also in humans. Using the sequential organ failure assessment (SOFA)-score as a measure of disease severity, we estimated the pure/organic indirect effect of alpha-DEspR using data from patients with COVID-19 not treated with alpha-DEspR. Our analysis illustrates the pre-clinical promise of alpha-DEspR, to be used as an argument to fund an early-stage randomized clinical trial to collect on-treatment outcomes and estimate the overall effect of alpha-DEspR – thus giving insight into clinical trial design. This illustrates how causal mediation analysis can be used as a potential translational bridge from petri dish and/or animal model testing towards clinical trial testing.