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A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments
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Prediction errors for state occupation and transition probabilities in multi-state models.

Cristian Spitoni1, Violette Lammens1, Hein Putter2

  • 1Department of Mathematics, Budapestlaan 6, 3584 CD, Utrecht, The Netherlands.

Biometrical Journal. Biometrische Zeitschrift
|October 26, 2017
PubMed
Summary
This summary is machine-generated.

This study introduces novel methods for estimating prediction errors in multistate time-to-event models. The proposed estimators, utilizing inverse probability weighting and pseudo-values, are consistent and validated on clinical trial data for liver cirrhosis.

Keywords:
dynamic predictioninverse probability of censoring weighted estimatormulti-state modelsprediction errorpseudo-observations

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Area of Science:

  • Biostatistics
  • Survival Analysis
  • Clinical Trials

Background:

  • Accurate prediction of patient outcomes is crucial in multistate models.
  • Estimating prediction errors for state occupation and transition probabilities with right-censored data presents statistical challenges.

Purpose of the Study:

  • To develop and validate methods for estimating prediction errors in multistate time-to-event data.
  • To assess prediction errors using Brier and Kullback-Leibler scores for both state occupation and transition probabilities.
  • To investigate the performance of estimators in the presence of right-censored data.

Main Methods:

  • Proposed two classes of estimators: inverse probability weighting and pseudo-values.
  • Investigated consistency properties of the proposed estimators.
  • Developed cross-validated estimators for dynamic prediction errors in multistate models.

Main Results:

  • Proved the properness of Brier and Kullback-Leibler scores for prediction error estimation.
  • Demonstrated consistency of the proposed inverse probability weighting and pseudo-value estimators.
  • Illustrated the methods using data from the CSL1 randomized clinical trial.

Conclusions:

  • The developed methods provide reliable estimation of prediction errors for multistate time-to-event models.
  • The proposed estimators are suitable for handling right-censored data.
  • The study offers practical tools for evaluating dynamic predictions in clinical research.