Estrogen Therapy Trial Data for Prostate Cancer with Competing Events

A methodological paper by Mats Julius Stensrud of Harvard University proposes separable effects for causal inference in time-to-event settings with competing events. The approach is illustrated using data from a randomized clinical trial on estrogen therapy for individuals with prostate cancer. The dataset likely contains patient-level event times and treatment assignments.

Use Cases

• Estimating separable direct effects of a treatment on an event of interest based on the proposed methodology
• Estimating separable indirect effects mediated through competing events based on the proposed methodology
• Comparing separable effects to existing causal estimands in the presence of competing events
• Applying causal inference methods to clinical trial data, as illustrated with the prostate cancer example

Strengths

• Methodology developed by a researcher at Harvard University
• Illustrated with a real-world randomized clinical trial example

Limitations

• Column-level documentation is absent; field semantics must be inferred after download
• Row count is unknown, which may limit suitability assessment
• Data may reflect temporal or population bias inherent to the specific clinical trial used

Provenance

Source

Mats Julius Stensrud, Harvard University

Collection Method

Data from a randomized clinical trial, as referenced in the methodological paper

Time Range

Temporal coverage of the clinical trial is unknown

Freshness

Last update date is unknown; freshness unverified

Geography

Spatial coverage of the clinical trial is unknown