Pages that link to "Item:Q3632627"
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The following pages link to Individual Prediction in Prostate Cancer Studies Using a Joint Longitudinal Survival–Cure Model (Q3632627):
Displaying 25 items.
- Joint modeling of longitudinal proportional measurements and survival time with a cure fraction (Q525900) (← links)
- Risk prediction for prostate cancer recurrence through regularized estimation with simultaneous adjustment for nonlinear clinical effects (Q652366) (← links)
- Discussion of: ``Predictive comparison of joint longitudinal-survival modeling: a case study illustrating competing approaches'' (Q746088) (← links)
- Assessing the association between trends in a biomarker and risk of event with an application in pediatric HIV/AIDS (Q985033) (← links)
- Identifiability of cure models revisited (Q2252900) (← links)
- A continuous-time hidden Markov model for cancer surveillance using serum biomarkers with application to hepatocellular carcinoma (Q2272464) (← links)
- Joint modeling of longitudinal and cure-survival data (Q2320840) (← links)
- A new approach for joint modelling of longitudinal measurements and survival times with a cure fraction (Q2856535) (← links)
- A characterization of missingness at random in a generalized shared-parameter joint modeling framework for longitudinal and time-to-event data, and sensitivity analysis (Q2931059) (← links)
- Dynamic Predictions and Prospective Accuracy in Joint Models for Longitudinal and Time-to-Event Data (Q3100782) (← 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)
- A Bayesian Semiparametric Survival Model with Longitudinal Markers (Q3576919) (← links)
- Latent Class Models for Joint Analysis of Longitudinal Biomarker and Event Process Data (Q4468363) (← links)
- Joint models for longitudinal counts and left-truncated time-to event data with applications to health insurance (Q4606118) (← links)
- Real‐Time Individual Predictions of Prostate Cancer Recurrence Using Joint Models (Q4919589) (← links)
- A joint survival-longitudinal modelling approach for the dynamic prediction of rehospitalization in telemonitored chronic heart failure patients (Q4970806) (← links)
- Joint analysis of longitudinal data and competing terminal events in the presence of dependent observation times with application to chronic kidney disease (Q5138228) (← 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)
- (Q5701067) (← links)
- The joint modeling of a longitudinal disease progression marker and the failure time process in the presence of cure (Q5701166) (← links)
- Combining longitudinal studies of PSA (Q5701278) (← links)
- BILITE: a Bayesian randomized phase II design for immunotherapy by jointly modeling the longitudinal immune response and time-to-event efficacy (Q6617390) (← links)
- Backward joint model and dynamic prediction of survival with multivariate longitudinal data (Q6628473) (← links)
- Individual dynamic prediction for cure and survival based on longitudinal biomarkers (Q6665467) (← links)
