Pooling and Predictor selection functions |
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Pooling and Predictor selection function for backward or forward selection of Linear regression models across multiply imputed data. |
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Pooling and Predictor selection function for backward or forward selection of Logistic regression models across multiply imputed data. |
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Pooling and Predictor selection function for backward or forward selection of Cox regression models across multiply imputed data. |
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Pooling and Predictor selection function for multilevel models in multiply imputed datasets |
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Multiparameter pooling methods called by psfmi_mm |
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Functions to evaluate Model performance |
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Internal validation and performance of logistic prediction models across Multiply Imputed datasets |
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Pooling performance measures across multiply imputed datasets |
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Provides pooled adjusted intercept after shrinkage of pooled coefficients in multiply imputed datasets |
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External Validation of logistic prediction models in multiply imputed datasets |
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Functions to evaluate Model stability |
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Function to evaluate bootstrap predictor and model stability in multiply imputed datasets. |
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Function to evaluate bootstrap predictor and model stability. |
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Functions to compare prediction models |
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Compare the fit and performance of prediction models across Multipy Imputed data |
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Function to pool NRI measures over Multiply Imputed datasets |
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Net Reclassification Index for Cox Regression Models |
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Extra Functions |
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Function for backward selection of Linear and Logistic regression models. |
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Function for forward selection of Linear and Logistic regression models. |
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Predictor selection function for backward selection of Cox regression models in single complete dataset. |
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Predictor selection function for forward selection of Cox regression models in single complete dataset. |
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Calculates the Hosmer and Lemeshow goodness of fit test. |
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Calculates the pooled C-statistic (Area Under the ROC Curve) across Multiply Imputed datasets |
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Calculates the scaled Brier score |
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Nagelkerke's R-square calculation for logistic regression / glm models |
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R-square calculation for Cox regression models |
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Function to combine estimates by using Rubin's Rules |
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Combines the Chi Square statistics across Multiply Imputed datasets |
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Pools the Likelihood Ratio tests across Multiply Imputed datasets ( method D4) |
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Calculation of Net Reclassification Index measures |
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Kaplan-Meier risk estimates for Net Reclassification Index analysis |
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Kaplan-Meier (KM) estimate at specific time point |
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Risk calculation at specific time point for Cox model |
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Datasets used in examples and tutorials |
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Example dataset for psfmi_perform function, method boot_MI |
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Data from a placebo-controlled RCT with leukemia patients |
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Dataset of patients with a aortadissection |
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Data of a non-experimental study in more than 300 elderly women |
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Data about concentration of ß2-microglobuline in urine as indicator for possible damage to the kidney |
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Long dataset of persons from the The Amsterdam Growth and Health Longitudinal Study (AGHLS) |
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Wide dataset of persons from the The Amsterdam Growth and Health Longitudinal Study (AGHLS) |
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Dataset of low back pain patients with missing values |
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Dataset of elderly patients with a hip fracture |
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External Dataset of elderly patients with a hip fracture |
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Dataset of the Hoorn Study |
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Data of a patient-control study regarding the relationship between MI and smoking |
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Data of the development of lung and heartvolume of unborn babies |
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Data of a study among women with breast cancer |
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Data of 613 patients with meningitis |
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Dataset with blood pressure measurements |
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Dataset with blood pressure measurements |
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Survival data about smoking |
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Dataset of persons from the The Amsterdam Growth and Health Longitudinal Study (AGHLS) |
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Example dataset for the psfmi_mm function |
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Example dataset for psfmi_coxr function |
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Example dataset for psfmi_lr function |
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Example dataset for mivalext_lr function |
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Example dataset of Low Back Pain Patients for external validation |
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Deprecated functions |
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Predictor selection function for backward selection of Linear and Logistic regression models. |
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Internal validation and performance of logistic prediction models across Multiply Imputed datasets |