Pages that link to "Item:Q2717985"
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The following pages link to The binary regression quantile plot: Assessing the importance of predictors in binary regression visually (Q2717985):
Displaying 18 items.
- Logistic regression analysis with standardized markers (Q386753) (← links)
- Adopting nested case-control quota sampling designs for the evaluation of risk markers (Q746492) (← links)
- Graphics for studying logistic regression models (Q1766985) (← links)
- Partial summary measures of the predictiveness curve (Q2857490) (← links)
- Comparing biomarkers as principal surrogate endpoints (Q2893405) (← links)
- Two Criteria for Evaluating Risk Prediction Models (Q3100813) (← links)
- Binary regression: Total gain in positive and negative predictive values (Q3145585) (← links)
- Methods for Evaluating Prediction Performance of Biomarkers and Tests (Q4984843) (← links)
- Estimating Improvement in Prediction with Matched Case-Control Designs (Q4984844) (← links)
- Generalizing the standardized hazard ratio to multivariate proportional hazards regression, with an application to clinical~genomic studies (Q5126953) (← links)
- Evaluating the Predictiveness of a Continuous Marker (Q5449919) (← links)
- The residual‐based predictiveness curve: A visual tool to assess the performance of prediction models (Q5739262) (← links)
- A Parametric ROC Model‐Based Approach for Evaluating the Predictiveness of Continuous Markers in Case–Control Studies (Q5850962) (← links)
- 7. Categorical Data Analysis (Q5899589) (← links)
- Estimating improvement in prediction with matched case-control designs (Q5963060) (← links)
- Flexible <i>cloglog</i> links for binomial regression models as an alternative for imbalanced medical data (Q6141308) (← links)
- Quantifying risk stratification provided by diagnostic tests and risk predictions: comparison to AUC and decision curve analysis (Q6624664) (← links)
- Copula modeling of receiver operating characteristic and predictiveness curves (Q6629893) (← links)
