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Prediction Intervals

The interval estimate of any variable is known as the prediction interval. It helps decide if a point estimate is dependable.
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Joint model for recurrent event data with a cured fraction and a terminal event.

Biometrical journal. Biometrische Zeitschrift·2019

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Updated: Jan 20, 2026

Prediction Intervals

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Dynamic prediction of interval-censored failure time data with longitudinal marker.

¹Department of statistics, Sookmyung womens' University, South Korea.

Statistical Methods in Medical Research

|January 19, 2026

Summary

This study introduces dynamic measures to evaluate patient prognosis prediction using longitudinal markers and interval-censored failure time data. The new methods improve accuracy for predicting events like dementia.

Keywords:

Brier score dynamic area under curve interval-censored data joint model landmarking approach longitudinal marker

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Intravital Longitudinal Imaging of Vascular Dynamics in the Calvarial Bone Marrow

Intravital Longitudinal Imaging of Vascular Dynamics in the Calvarial Bone Marrow

Published on: April 11, 2025

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Last Updated: Jan 20, 2026

Prediction Intervals

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Censoring Survival Data

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Intravital Longitudinal Imaging of Vascular Dynamics in the Calvarial Bone Marrow

Intravital Longitudinal Imaging of Vascular Dynamics in the Calvarial Bone Marrow

Published on: April 11, 2025

Area of Science:

Biostatistics
Clinical Epidemiology
Longitudinal Data Analysis

Background:

Accurate patient prognosis prediction is crucial for clinical decision-making.
Existing prediction models need to dynamically update with evolving patient conditions.
Joint models for longitudinal markers and time-to-event data are common but require methods for dynamic evaluation.

Purpose of the Study:

To develop dynamic measures for assessing the predictive accuracy of longitudinal markers.
To address the challenge of interval-censored failure time data in prognostic modeling.
To evaluate prediction performance in a real-world dementia prediction scenario.

Main Methods:

Proposed novel dynamic area under the curve and Brier score metrics.
Incorporated the specific data structure of interval-censored failure time data.
Conducted simulation studies comparing joint modeling and landmarking approaches.

Main Results:

The proposed dynamic measures effectively reflect the incomplete data structure.
Simulation results demonstrated the performance of the joint model compared to landmarking.
The methods were successfully applied to predict dementia using cognitive scores.

Conclusions:

Dynamic evaluation metrics are essential for updating prognostic models.
The proposed methods offer a robust way to assess predictive accuracy with interval-censored data.
This approach enhances the prediction of clinical events, such as dementia onset.