Charles Wolock Seminar

You are all invited to an exciting seminar at Biostats on Friday, August 14 @ 14:00: 

Charles Wolock, Department of Biostatistics and Computational Biology at the University of Rochester.

"Leveraging machine learning to estimate survival curves with current status data"

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.

Room: Biostat library (5.2.46)

Unless otherwise stated, seminars will be held at CSS (det gamle Kommunehospital), Øster Farimagsgade 5, 1353 Copenhagen K, room 5.2.46. Tea will be served in the library of the section of Biostatistics half an hour before the seminar starts.