Pages that link to "Item:Q4919589"
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The following pages link to Real‐Time Individual Predictions of Prostate Cancer Recurrence Using Joint Models (Q4919589):
Displaying 26 items.
- Risk prediction for prostate cancer recurrence through regularized estimation with simultaneous adjustment for nonlinear clinical effects (Q652366) (← links)
- Modelling the recurrence of bladder cancer (Q959996) (← links)
- GPU accelerated estimation of a shared random effect joint model for dynamic prediction (Q2157530) (← links)
- Updating risk prediction tools: a case study in prostate cancer (Q2893542) (← links)
- Choice of prognostic estimators in joint models by estimating differences of expected conditional Kullback-Leibler risks (Q2912329) (← links)
- Development and validation of a dynamic prognostic tool for prostate cancer recurrence using repeated measures of posttreatment PSA: a joint modeling approach (Q3305046) (← links)
- Sequential Monte Carlo methods in Bayesian joint models for longitudinal and time-to-event data (Q3389298) (← links)
- Individual Prediction in Prostate Cancer Studies Using a Joint Longitudinal Survival–Cure Model (Q3632627) (← links)
- Frequentist and Bayesian approaches for a joint model for prostate cancer risk and longitudinal prostate-specific antigen data (Q5130239) (← links)
- Time-varying Hazards Model for Incorporating Irregularly Measured, High-Dimensional Biomarkers (Q5134494) (← links)
- A Bayesian hierarchical model for prediction of latent health states from multiple data sources with application to active surveillance of prostate cancer (Q5283323) (← links)
- Estimation of the optimal regime in treatment of prostate cancer recurrence from observational data using flexible weighting models (Q5283324) (← links)
- Joint partially linear model for longitudinal data with informative drop‐outs (Q5347404) (← links)
- On longitudinal prediction with time‐to‐event outcome: Comparison of modeling options (Q5347405) (← links)
- Robust joint modelling of longitudinal and survival data: Incorporating a time‐varying degrees‐of‐freedom parameter (Q6068290) (← links)
- Review and Comparison of Computational Approaches for Joint Longitudinal and Time‐to‐Event Models (Q6086623) (← links)
- Robust joint modelling of left-censored longitudinal data and survival data with application to HIV vaccine studies (Q6104088) (← links)
- Dynamic risk prediction triggered by intermediate events using survival tree ensembles (Q6161880) (← links)
- Joint hierarchical Gaussian process model with application to personalized prediction in medical monitoring (Q6541450) (← links)
- Optimizing dynamic predictions from joint models using super learning (Q6618415) (← links)
- A comparison of two approaches to dynamic prediction: joint modeling and landmark modeling (Q6625749) (← links)
- Tackling dynamic prediction of death in patients with recurrent cardiovascular events (Q6626876) (← links)
- Dynamic predictive probabilities to monitor rapid cystic fibrosis disease progression (Q6627491) (← links)
- Bayesian survival analysis with BUGS (Q6627850) (← links)
- Landmarking 2.0: bridging the gap between joint models and landmarking (Q6628335) (← links)
- Shared decision making of burdensome surveillance tests using personalized schedules and their burden and benefit (Q6628349) (← links)
