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Related Experiment Video

Updated: Feb 21, 2026

Continuous Theta Burst Stimulation of the Posterior Medial Frontal Cortex to Experimentally Reduce Ideological Threat Responses

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When to Censor?

Catherine R Lesko¹, Jessie K Edwards², Stephen R Cole²

¹Department of Epidemiology, Bloomberg School of Public Health, Johns Hopkins University, Baltimore, Maryland.

American Journal of Epidemiology

|October 12, 2017

Summary

This summary is machine-generated.

Handling loss to follow-up in time-to-event analyses is crucial. Person-time should be censored at the loss to follow-up criterion if the event is captured externally, but at the last study encounter if the event requires in-person measurement.

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Last Updated: Feb 21, 2026

Continuous Theta Burst Stimulation of the Posterior Medial Frontal Cortex to Experimentally Reduce Ideological Threat Responses

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Published on: September 28, 2018

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Area of Science:

Biostatistics
Epidemiology
Clinical Trials

Background:

Loss to follow-up is a common challenge in time-to-event analyses, impacting data integrity.
Current methods for handling person-time after the last study encounter lack consensus, potentially introducing bias.

Purpose of the Study:

To clarify appropriate censoring strategies for person-time in time-to-event analyses with loss to follow-up.
To provide guidance on handling data between the last study encounter and the defined loss to follow-up point.

Main Methods:

The study employed simulation and a case example to evaluate different censoring schemes.
Analysis focused on time-to-event data where events are captured either externally or during study encounters.

Main Results:

When events are captured outside study encounters (e.g., registries), censoring should occur at the loss to follow-up criterion.
When events must be measured during study encounters (e.g., biomarkers), censoring should occur at the last study encounter.

Conclusions:

The choice of censoring strategy critically depends on how the event of interest is ascertained.
Inappropriate censoring can lead to significant and potentially uncorrectable bias in time-to-event analyses.