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Time-dependent Predictive Accuracy Metrics in the Context of Interval Censoring and Competing Risks

Autor: Yang, Zhenwei, Rizopoulos, Dimitris, Newcomb, Lisa F., Erler, Nicole S.

Evaluating the performance of a prediction model is a common task in medical statistics. Standard accuracy metrics require the observation of the true outcomes. This is typically not possible in the setting with time-to-event outcomes due to censorin

Externí odkaz: http://arxiv.org/abs/2501.01280

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A Bayesian Joint Modelling for Misclassified Interval-censoring and Competing Risks

Autor: Yang, Zhenwei, Rizopoulos, Dimitris, Heijnsdijk, Eveline A. M., Newcomb, Lisa F., Erler, Nicole S.

In active surveillance of prostate cancer, cancer progression is interval-censored and the examination to detect progression is subject to misclassification, usually false negatives. Meanwhile, patients may initiate early treatment before progression

Externí odkaz: http://arxiv.org/abs/2404.09362

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Personalized Biopsy Schedules Using an Interval-censored Cause-specific Joint Model

Autor: Yang, Zhenwei, Rizopoulos, Dimitris, Heijnsdijk, Eveline A. M., Newcomb, Lisa F., Erler, Nicole S.

Active surveillance (AS), where biopsies are conducted to detect cancer progression, has been acknowledged as an efficient way to reduce the overtreatment of prostate cancer. Most AS cohorts use fixed biopsy schedules for all patients. However, the i

Externí odkaz: http://arxiv.org/abs/2209.00105

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