Abstract: Dynamic weighted ordinary least squares (dWOLS) was proposed as a simple analytic tool for estimating optimal adaptive treatment strategies. The approach aimed to combine the double robustness of G-estimation with the ease of implementation of Q-learning, however early methodology was limited to only the continuous outcome/binary treatment setting. In this talk, I will give an overview of the statistical view of precision medicine, describe the dWOLS approach, and provide case studies that illustrate recent extensions of dWOLS to censored (“survival”) outcomes and to continuous-valued treatments (doses). The applications demonstrate both the strengths and challenges of estimating optimal treatment strategies using large, clinical databases.

About the speaker: Erica E. M. Moodie is a Professor of Biostatistics and a Canada Research Chair (Tier 1) in Statistical Methods for Precision Medicine. She obtained her MPhil in Epidemiology in 2001 from the University of Cambridge and a PhD in Biostatistics in 2006 from the University of Washington, before joining the faculty at McGill. Her main research interests are in causal inference and longitudinal data with a focus on precision medicine. She is the 2020 recipient of the CRM-SSC Prize in Statistics and an Elected Member of the International Statistical Institute. Dr Moodie serves as an Associate Editor of Biometrics and a Statistical Editor of Journal of Infectious Diseases. She holds a chercheur de merite career award from the Fonds de recherche du Quebec-Sante.