Leveraging machine learning to estimate survival curves with current status data

Thomas M. Menino Convention & Exhibition Center 

In many epidemiological study designs, time-to-event outcomes may be subject to current status sampling: rather than observing the outcome itself, the investigator observes each study participant at a single monitoring time, recording a binary indicator of whether the event has occurred by that time. Such study design results in an extreme form of interval censoring. Existing nonparametric methods for current status data typically require independence between the monitoring time and the event time, which may be unrealistic in practice. We propose an approach to estimating the survival curve of a time-to-event outcome under current status sampling using tools from semiparametric efficiency theory and shape-constrained estimation. This approach is closely related to existing methods for estimating a causal dose-response curve under an assumption of monotonicity. Our proposed method allows for monitoring processes that are informed by measured covariates and employs machine learning tools to flexibly estimate nuisance parameters. We devise a sensitivity analysis approach investigating the degree to which the resulting estimates change under deviations from conditionally uninformative monitoring. We use the proposed methods to estimate the duration of COVID-19 symptoms in a university population.