Senior Scientist II Metrum Research Group Tariffville, Connecticut
In drug development, a summary measure of drug exposure is often linked to an effect (and used to predict an outcome) in order to determine the benefit-risk profile of a treatment in a specific population. A variety of exposure measures can be used to drive the exposure-response (ER) relationship, such as maximum concentration (Cmax) or average concentration within a dosing interval (Cavg). A logical and attractive exposure measure for ER analysis of adverse events (AE) is average drug concentration until time of the event (CavgTE). We can demonstrate, through simulation, that using CavgTE is inappropriate in a study with a single dose level. The choice of exposure measure in an ER analysis should not be driven only by goodness-of-fit, but by carefully choosing exposure measures that are not influenced by the outcome we are trying to predict.
Learning Objectives:
To understand basis of "causal inference" and "counfounding" and why these are critical in planning clinical trials and analyzing study data.
To understand the premise of exposure-response analysis, using application to an oncology trial.
Using Cave-te (average drug concentration until event) as an exposure metric to drive a safety event in an exposure-response analysis is both intuitive and appealing, it may cause confounding.