Data Integration for Time-to-Event Outcomes
Figure 1. Motivating data structure for data integration methods development.
Surrogate Paradox Risk
Clinical trials often collect surrogate endpoints other than the true endpoint of interest. Surrogate endpoints are helpful because they usually occur more frequently resulting in reductions in required study sample size and duration.
When using surrogate endpoints the most important assumption is that the treatment effect on surrogate accurately predicts the treatment effect on the true endpoint. There are settings in which this assumption is violated even though the treatment is positively correlated with the surrogate and the surrogate is positively correlated with the true endpoint—a phenomenon labeled “surrogate paradox”.
When data are available on multiple clinical trials in which both the true and surrogate endpoints have been measured, the quality of surrogates can be assessed by the degree of correlation between trial-level treatment effects on the two outcomes under the causal association framework. However, high correlation still does not preclude the possibility of surrogate paradox, meaning that a surrogate that has been deemed high quality by existing measures can still provide incorrect conclusions about the treatment effect in a new study.
I develop methods for identifying the risk of surrogate paradox in subpopulations when data on multiple trials are available. Incorporating covariate information can provide valuable insights into the mechanism of the surrogate paradox and identify groups that are particularly vulnerable.
Health Disparity Applications
Figure 2. Observed probabilities of transition between various tobacco use states from four waves of the PATH study incorporating survey weights for a nationally representative population.
Figure 3. Tobacco product transitions of interest in the EXHALE cohort study.