The following pages link to (Q5701067):
Displaying 36 items.
- Stochastic model for analysis of longitudinal data on aging and mortality (Q97673) (← links)
- Fast fitting of joint models for longitudinal and event time data using a pseudo-adaptive Gaussian quadrature rule (Q425636) (← links)
- Joint modeling of longitudinal proportional measurements and survival time with a cure fraction (Q525900) (← links)
- Estimating a unitary effect summary based on combined survival and quantitative outcomes (Q1800122) (← links)
- A stochastic model for prostate-specific antigen levels (Q1877147) (← links)
- The new Neyman type A beta Weibull model with long-term survivors (Q2255910) (← links)
- Vertical modeling: analysis of competing risks data with a cure fraction (Q2274658) (← links)
- Joint modeling of longitudinal and cure-survival data (Q2320840) (← links)
- A stochastic model for PSA levels: behavior of solutions and population statistics (Q2433027) (← links)
- Joint model for left-censored longitudinal data, recurrent events and terminal event: predictive abilities of tumor burden for cancer evolution with application to the FFCD 2000--05 trial (Q2827208) (← 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 Two-Part Joint Model for the Analysis of Survival and Longitudinal Binary Data with Excess Zeros (Q3506512) (← links)
- Semiparametric Modeling of Longitudinal Measurements and Time‐to‐Event Data–A Two‐Stage Regression Calibration Approach (Q3549419) (← links)
- A Bayesian Semiparametric Survival Model with Longitudinal Markers (Q3576919) (← links)
- Individual Prediction in Prostate Cancer Studies Using a Joint Longitudinal Survival–Cure Model (Q3632627) (← links)
- Latent Class Models for Joint Analysis of Longitudinal Biomarker and Event Process Data (Q4468363) (← links)
- Bayesian Influence Measures for Joint Models for Longitudinal and Survival Data (Q4649077) (← 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 MODEL PREDICTION AND APPLICATION TO INDIVIDUAL-LEVEL LOSS RESERVING (Q5067883) (← links)
- Semiparametric random censorship models for survival data with long-term survivors (Q5083904) (← links)
- Comparing crossing hazard rate functions by joint modelling of survival and longitudinal data (Q5107529) (← links)
- Frequentist and Bayesian approaches for a joint model for prostate cancer risk and longitudinal prostate-specific antigen data (Q5130239) (← links)
- Joint modelling of longitudinal biomarker and gap time between recurrent events: copula-based dependence (Q5130307) (← links)
- A semiparametric Bayesian approach for joint modeling of longitudinal trait and event time (Q5138221) (← links)
- Simulated maximum likelihood estimation in joint models for multiple longitudinal markers and recurrent events of multiple types, in the presence of a terminal event (Q5138745) (← links)
- Joint models for multiple longitudinal processes and time-to-event outcome (Q5221561) (← links)
- Joint Modeling of Survival and Longitudinal Data: Likelihood Approach Revisited (Q5295357) (← 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)
- Review and Comparison of Computational Approaches for Joint Longitudinal and Time‐to‐Event Models (Q6086623) (← links)
- Mixture cure model methodology in survival analysis: some recent results for the one-sample case (Q6500004) (← links)
- Investigation of one-stage meta-analysis methods for joint longitudinal and time-to-event data through simulation and real data application (Q6625549) (← links)
- Joint Models for Event Prediction From Time Series and Survival Data (Q6631905) (← links)
