Causal inference methods to optimize clinical decision-making in treatment initiation based on routinely collected data

You are all invited to an exciting _online_ seminar at Biostats on Monday, October 14 @ 16:30: 

NOTE: This will be a zoom meeting: https://ucph-ku.zoom.us/j/8274562019?pwd=ZEJuZkZUY05WNE02YzNWUGhONWoyUT09

Pawel Morzywolek, Department of Statistics at the University of Washington

"Causal inference methods to optimize clinical decision-making in treatment initiation based on routinely collected data"

Renal replacement therapy (RRT) is a treatment commonly used for managing critically ill patients with severe acute kidney injury (AKI), particularly those experiencing metabolic or fluid-related complications. RRT may rapidly correct some of the life-threatening issues associated with AKI, such as fluid overload. However, it is a very invasive treatment and may therefore be harmful to some patients. The timing of RRT initiation in critically ill patients with AKI remains a long-standing dilemma for nephrologists. Multiple randomized trials have attempted to address this question, but they compare only a limited number of treatment initiation strategies. In light of this, we use routinely collected observational data from the Ghent University Hospital intensive care units to investigate treatment strategies for starting RRT in critically ill AKI patients. We develop a methodology for identifying the optimal treatment initiation strategy from several pre-specified options in the presence of competing risks. We then apply it to evaluate a total of 81 RRT initiation strategies, expressed in terms of serum potassium, pH, and urine output, allowing us to identify the optimal thresholds for these criteria. Furthermore, we develop a unified framework of weighted Neyman-orthogonal learners for estimating heterogeneous treatment effects to support clinical decision-making regarding treatment initiation.