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Related Experiment Video

Updated: Jun 12, 2026

High Density Event-related Potential Data Acquisition in Cognitive Neuroscience

High Density Event-related Potential Data Acquisition in Cognitive Neuroscience

Published on: April 16, 2010

Event dependent sampling of recurrent events.

Kajsa Kvist¹, Per Kragh Andersen, Jules Angst

¹Department of Biostatistics, University of Copenhagen, Copenhagen, Denmark. kajsa.kvist@psy.ku.dk

Lifetime Data Analysis

|June 8, 2010

Summary

This study addresses event-dependent sampling in recurrent event analysis, crucial for understanding disease recurrence risk. Methods are developed and validated to correct for selection bias in psychiatric epidemiology research.

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Published on: August 1, 2011

Area of Science:

Biostatistics
Epidemiology
Psychiatric Research

Background:

Recurrent events, such as hospital admissions for affective disease, are common in clinical practice.
Analyzing the risk of recurrence based on event count can be biased by how study subjects are selected.
Event-dependent sampling, where processes are chosen if an event occurs within a specific interval, is a common issue.

Purpose of the Study:

To investigate the impact of event-dependent sampling on the analysis of recurrent events.
To develop and evaluate statistical methods for correcting selection bias in recurrent event data.
To apply these methods to real-world data from psychiatric epidemiology.

Main Methods:

Investigated event-dependent sampling in recurrent event processes.
Developed two methods to correct for selection bias: conditional distribution and inverse-probability-of-selection weighting.
Validated methods using simulation studies and applied them to affective disease data.

Main Results:

The developed methods demonstrate the ability to correct for selection bias in recurrent event analysis.
Simulations confirmed the effectiveness of both the conditional distribution and inverse-probability-of-selection weighting approaches.
Application to affective disease data provided insights into recurrence risk.

Conclusions:

Event-dependent sampling requires careful statistical consideration in recurrent event analysis.
The proposed methods offer valid approaches to mitigate selection bias in such studies.
These findings have implications for psychiatric epidemiology and the study of recurrent diseases.