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

Yang-Jin Kim

Statistical Methods In Medical Research2026https://doi.org/10.1177/09622802251412849article
ABDC B
Weight
0.50

What the paper says

A main interest in clinical practice is the prediction of patient prognosis conductive to decision making. Therefore, a relevant prediction model should be able to reflect the updated patient's condition. A joint model of longitudinal markers and time-to-event data has been widely applied to estimate the association between the risk of the event and the markers' change. The purpose of this work is to provide dynamic measures for evaluating the predictive accuracy of longitudinal markers in a context of interval-censored failure time data. We propose dynamic area under curve and Brier score reflecting incomplete data structure of interval-censored data. Simulation study compares the prediction performance of joint model and landmarking method. As a real data example, the suggested method is applied to predict the occurrence of dementia using repeatedly measured cognitive scores.

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https://doi.org/https://doi.org/10.1177/09622802251412849

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@article{yang-jin2026,
  title        = {{Dynamic prediction of interval-censored failure time data with longitudinal marker}},
  author       = {Yang-Jin Kim},
  journal      = {Statistical Methods In Medical Research},
  year         = {2026},
  doi          = {https://doi.org/https://doi.org/10.1177/09622802251412849},
}

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

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Evidence weight

0.50

Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40

F · citation impact0.50 × 0.4 = 0.20
M · momentum0.50 × 0.15 = 0.07
V · venue signal0.50 × 0.05 = 0.03
R · text relevance †0.50 × 0.4 = 0.20

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