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An R-Based Landscape Validation of a Competing Risk Model

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Assessing the accuracy of predictive models with interval-censored data.

Ying Wu¹, Richard J Cook²

¹School of Statistics and Data Science, Nankai University, Tianjin, 300071, China.

Biostatistics (Oxford, England)

|March 15, 2020

Summary

We developed new methods to accurately assess predictive models using interval-censored data. These techniques improve predictions for event status at a specific time, aiding clinical research.

Keywords:

Augmented inverse probability weighted estimator Intermittent assessment Interval censoring Inverse probability weighted estimator Prediction error ROC curve

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Area of Science:

Biostatistics
Epidemiology
Clinical Research Methodology

Background:

Assessing predictive model accuracy with interval-censored data presents unique challenges.
Existing methods may not adequately handle the complexities of recurrent events and loss to follow-up.

Purpose of the Study:

To develop and evaluate novel methods for assessing the predictive accuracy of event time models using interval-censored validation data.
To compare the performance of imputation-based, inverse probability weighted (IPW), and augmented inverse probability weighted (AIPW) estimators.

Main Methods:

Developed imputation-based, IPW, and AIPW estimators for mean prediction error and area under the ROC curve.
Utilized multistate models to derive weights for IPW and AIPW, accounting for event, assessment, and loss-to-follow-up processes.
Empirically investigated the performance of the proposed methods.

Main Results:

The developed methods provide robust assessments of predictive accuracy for event status at a landmark time.
IPW and AIPW estimators, utilizing multistate models, demonstrated effective handling of complex data structures.
The study provides a framework for evaluating predictive models in settings with interval-censored data.

Conclusions:

The proposed imputation-based, IPW, and AIPW methods offer reliable tools for evaluating event time model predictive accuracy with interval-censored data.
These methods are applicable to various clinical research scenarios, including predicting disease progression.
The study highlights the importance of accounting for recurrent assessments and loss to follow-up in predictive modeling.