Pages that link to "Item:Q4668409"
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The following pages link to Predictive Accuracy and Explained Variation in Cox Regression (Q4668409):
Displaying 28 items.
- Prediction Accuracy Measures for a Nonlinear Model and for Right-Censored Time-to-Event Data (Q151994) (← links)
- The use of Roc for defining the validity of the prognostic index in censored data (Q544625) (← links)
- Validation of prognostic indices using the frailty model (Q841049) (← links)
- Asymptotically optimal model selection method with right censored outcomes (Q1763100) (← links)
- Explained variation and predictive accuracy in general parametric statistical models: The role of model misspecification (Q1770878) (← links)
- Comparison of joint modelling and landmarking approaches for dynamic prediction using bootstrap simulation (Q2089366) (← links)
- Extensions of the absolute standardized hazard ratio and connections with measures of explained variation and variable importance (Q2218820) (← 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)
- 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)
- A Robust Alternative to the Schemper-Henderson Estimator of Prediction Error (Q3013982) (← links)
- Time-Dependent Predictive Accuracy in the Presence of Competing Risks (Q3076028) (← links)
- A Measure of Explained Variation for Event History Data (Q3100776) (← 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)
- Prediction accuracy measures for time-to-event models with left-truncated and right-censored data (Q3389680) (← links)
- Explained variation for recurrent event data (Q3451371) (← links)
- Quantifying the Predictive Performance of Prognostic Models for Censored Survival Data with Time-Dependent Covariates (Q3506510) (← links)
- Variability explained by covariates in linear mixed-effect models for longitudinal data (Q3589851) (← links)
- Discrimination measures for survival outcomes: Connection between the AUC and the predictiveness curve (Q5391154) (← links)
- Measures of prediction error for survival data with longitudinal covariates (Q5391158) (← links)
- Partly Conditional Survival Models for Longitudinal Data (Q5714620) (← links)
- Survival Model Predictive Accuracy and ROC Curves (Q5715356) (← links)
- Dynamic monitoring of the effects of adherence to medication on survival in heart failure patients: A joint modeling approach exploiting time‐varying covariates (Q6071298) (← links)
- Conditional transformation models for survivor function estimation (Q6593498) (← links)
- Explained variation and degrees of necessity and of sufficiency for competing risks survival data (Q6625445) (← links)
- Degrees of necessity and of sufficiency: further results and extensions, with an application to Covid-19 mortality in Austria (Q6628118) (← links)
- Quantifying degrees of necessity and of sufficiency in cause-effect relationships with dichotomous and survival outcomes (Q6628731) (← links)
