A class of nonparametric methods for evaluating the effect of continuous treatments on survival outcomes

Thomas M. Menino Convention & Exhibition Center 

In randomized trials and observational studies, it is often necessary to evaluate how an exposure affects a time-to-event outcome, which is only partially observed due to right censoring. For instance, in infectious disease studies, it is of interest to characterize the relationship between the time until diagnosis of pathogen infection and a preceding measurement of a biomarker quantifying an immune response against that pathogen. Such analyses are commonly conducted within the counterfactual outcomes framework, seeking inferences about the counterfactual probability of survival through a given time point, at any given biomarker/exposure level. To determine whether a causal effect is present, one can test whether this quantity differs by exposure level. When the exposure is continuous, hypothesis testing and estimation in a nonparametric model is challenging. Recent theoretical developments made important strides for tackling this problem by considering either continuous exposures with uncensored outcomes or binary exposures with time-to-event outcomes. However, the existing work does not accommodate the simultaneous presence of continuous exposures and time-to-event outcomes. To fill this gap, we develop a new method for assessing whether the counterfactual survival probabilities remain constant across the range of a continuous exposure. We apply our methods to data from a pair of HIV monoclonal antibody prevention efficacy trials, and to data from a COVID-19 vaccine trial.

Survival Analysis

Nonparametric Statistics

Causal Incidence

Immune Correlates

HIV

COVID-19