Incorporating short‐term outcome information to predict long‐term survival with discrete markers
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Publication:5391159
DOI10.1002/bimj.201000150zbMath1209.62321OpenAlexW2014076040WikidataQ36321574 ScholiaQ36321574MaRDI QIDQ5391159
Layla Parast, Su-Chun Cheng, Tianxi Cai
Publication date: 6 April 2011
Published in: Biometrical Journal (Search for Journal in Brave)
Full work available at URL: http://europepmc.org/articles/pmc3472667
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Related Items (7)
Discussion of “A Risk-Based Measure of Time-Varying Prognostic Discrimination for Survival Models,” by C. Jason Liang and Patrick J. Heagerty ⋮ Time-dependent diagnostic accuracy analysis with censored outcome and censored predictor ⋮ Landmark Prediction of Long-Term Survival Incorporating Short-Term Event Time Information ⋮ Dynamic Pseudo‐Observations: A Robust Approach to Dynamic Prediction in Competing Risks ⋮ Landmark Estimation of Survival and Treatment Effect in a Randomized Clinical Trial ⋮ landpred ⋮ Flexible association modelling and prediction with semi‐competing risks data
Cites Work
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- Dynamic predicting by landmarking as an alternative for multi-state modeling: an application to acute lymphoid leukemia data
- Uniform consistency of the kernel conditional Kaplan-Meier estimate
- On some global measures of the deviations of density function estimates
- On semi-competing risks data
- Competing Risks and Time-Dependent Covariates
- Estimating subject-specific survival functions under the accelerated failure time model
- Identifying Subjects Who Benefit from Additional Information for Better Prediction of the Outcome Variables
- Calibrating parametric subject-specific risk estimation
- Semiparametric Box–Cox power transformation models for censored survival observations
- A Markov Model for Analysing Cancer Markers and Disease States in Survival Studies
- Transformations in Density Estimation
- A kernel method for incorporating information on disease progression in the analysis of survival
- Simple Transformation Techniques for Improved Non‐parametric Regression
- Combining diagnostic test results to increase accuracy
- Nonparametric Estimation for the Three‐Stage Irreversible Illness–Death Model
- Dynamic Prediction by Landmarking in Event History Analysis
- Model evaluation based on the sampling distribution of estimated absolute prediction error
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