Development and validation of a dynamic prognostic tool for prostate cancer recurrence using repeated measures of posttreatment PSA: a joint modeling approach
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Publication:3305046
DOI10.1093/biostatistics/kxp009zbMath1437.62585OpenAlexW2136073265WikidataQ37229912 ScholiaQ37229912MaRDI QIDQ3305046
Cécile Proust-Lima, Jeremy M. G. Taylor
Publication date: 4 August 2020
Published in: Biostatistics (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1093/biostatistics/kxp009
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Cites Work
- Random-Effects Models for Longitudinal Data
- Joint modelling of multivariate longitudinal outcomes and a time-to-event: a nonlinear latent class approach
- Estimating the dimension of a model
- Quantifying the Predictive Performance of Prognostic Models for Censored Survival Data with Time-Dependent Covariates
- Covariate measurement errors and parameter estimation in a failure time regression model
- An Algorithm for Least-Squares Estimation of Nonlinear Parameters
- Latent Class Models for Joint Analysis of Longitudinal Biomarker and Event Process Data
- Predictive Accuracy and Explained Variation in Cox Regression
- Modeling the Relationship of Survival to Longitudinal Data Measured with Error. Applications to Survival and CD4 Counts in Patients with AIDS
- Dynamic Prediction by Landmarking in Event History Analysis
- Prospective Accuracy for Longitudinal Markers
- Joint modelling of longitudinal measurements and event time data
- Identification and efficacy of longitudinal markers for survival
- Partly Conditional Survival Models for Longitudinal Data
- Survival Model Predictive Accuracy and ROC Curves
- Determining the number of components in mixtures of linear models.
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