Pages that link to "Item:Q5701126"
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The following pages link to Identification and efficacy of longitudinal markers for survival (Q5701126):
Displaying 28 items.
- Penalised logistic regression and dynamic prediction for discrete-time recurrent event data (Q269755) (← links)
- Intermediate clinical events, surrogate markers and survival (Q1895388) (← links)
- Identification of potential longitudinal biomarkers under the accelerated failure time model in multivariate survival data (Q2807714) (← links)
- The Method to Identify a Biomarker and to Evaluate Its Efficiency for Survival by Using the Joint Model of the Accelerate Failure Time and Longitudinal Data (Q2815342) (← links)
- Using the Score Test to Identify the Longitudinal Biomarker Considering Accelerate Failure Time Model with the Frailty in Survival Analysis (Q2890113) (← links)
- Choice of prognostic estimators in joint models by estimating differences of expected conditional Kullback-Leibler risks (Q2912329) (← links)
- The Identification of Potential Longitudinal Biomarkers and Measurements of Effectiveness for Biomarkers as Surrogates in Multivariate Survival Data (Q2920068) (← links)
- Identification of Longitudinal Biomarkers in Survival Analysis for Competing Risks Data (Q2931542) (← links)
- Identification of longitudinal biomarkers for survival by a score test derived from a joint model of longitudinal and competing risks data (Q2953291) (← links)
- Using Frailty Models to Identify the Longitudinal Biomarkers in Survival Analysis (Q3064066) (← 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)
- Diagnostics for Joint Longitudinal and Dropout Time Modeling (Q3433198) (← links)
- Explained variation for recurrent event data (Q3451371) (← links)
- Quantifying and comparing dynamic predictive accuracy of joint models for longitudinal marker and time‐to‐event in presence of censoring and competing risks (Q3465731) (← links)
- Quantifying the Predictive Performance of Prognostic Models for Censored Survival Data with Time-Dependent Covariates (Q3506510) (← links)
- Nonparametric Two-Sample Tests of Longitudinal Data with Termination Events (Q4921658) (← links)
- How the mechanism of missing data on longitudinal biomarkers influences the survival analysis (Q5078061) (← links)
- Bayesian parametric accelerated failure time spatial model and its application to prostate cancer (Q5124785) (← links)
- Application of trajectories from growth curve in identification of longitudinal biomarker for the multivariate survival data (Q5138544) (← links)
- Measures of prediction error for survival data with longitudinal covariates (Q5391158) (← links)
- Dynamic Prediction by Landmarking in Event History Analysis (Q5430598) (← links)
- Prospective Accuracy for Longitudinal Markers (Q5459574) (← links)
- Evaluation of longitudinal surrogate markers (Q6050955) (← links)
- Joint models for longitudinal and discrete survival data in credit scoring (Q6167389) (← links)
- Joint models of multivariate longitudinal outcomes and discrete survival data with INLA: an application to credit repayment behaviour (Q6168507) (← links)
- Optimizing dynamic predictions from joint models using super learning (Q6618415) (← links)
- Joint models for longitudinal and time-to-event data in a case-cohort design (Q6625649) (← links)
- Incorporating longitudinal biomarkers for dynamic risk prediction in the era of big data: a pseudo-observation approach (Q6629825) (← links)