Title: Improving Causal Inference in Clinical and Health Sciences.

Abstract:

Mediation analysis is a statistical method used to estimate the causal effect of an exposure on an outcome by considering the role of an intermediate variable, or mediator. However, mediation analysis relies on the assumption that there are no unmeasured confounders, which is often not testable in real-world settings. In this talk, we propose a sensitivity analysis technique for nonrandomized latent growth curve mediation models (LGCMMs). LGCMMs are a type of mediation model that is commonly used in studies on alcohol addiction. Our proposed sensitivity analysis technique, called extended correlated augmented mediation sensitivity analysis (ECAMSA), extends a previous sensitivity analysis technique to account for the nonrandomized nature of LGCMMs. We demonstrate the use of ECAMSA through an empirical example. We find that ECAMSA can be used to assess the robustness of mediation analysis results to the violation of the no-omitted confounder assumption. We conclude that ECAMSA is a useful tool to improve causal inference in clinical and health sciences.

Brief Bio: Davood Tofighi

Davood Tofighi is an Associate Professor of Psychology at the University of New Mexico. His research focusses on developing and evaluating statistical methodologies that enhance the robustness of statistical inferences in various analytical domains. He is particularly interested in causal inference, application, and development of direct acyclic graph (DAG) in causal mediation analysis, statistical and Monte Carlo sensitivity analysis, generalized linear mixed-effects model, multivariate statistics, categorical data analysis, and the judicious application of machine learning techniques to health data.

Dr. Tofighi’s substantive research focuses on amplifying the replicability of statistical methods in the context of health and prevention science. He is also interested in optimizing drug delivery formulations, a critical area of study with far-reaching implications for cancer and COVID-19 research. As an Associate Editor for the Quantitative Psychology and Measurement specialty section of Frontiers in Psychology, Dr. Tofighi plays an active role in the academic community, ensuring the dissemination of rigorous research to a broader audience. His contributions have garnered notable recognition, with a total of 7,659 citations and an H-Index of 15, according to Google Scholar. Dr. Tofighi acknowledges the support of funding institutions such as NASA, NSF, and NIH, which have been instrumental in enabling his research pursuits. His academic journey reflects his multidisciplinary approach, with a Ph.D. in Quantitative Psychology from Arizona State University, complemented by an M.A. in Educational Psychology. Additionally, he has earned M.Sc. and B.Sc. degrees in Industrial and Systems Engineering.