Prediction Model Pooling, Selection and Performance Evaluation Across Multiply Imputed Datasets
Bootstrap validation in Multiply Imputed datasets
Predictor selection function for backward selection of Linear and Logi...
Function to clean variables
Predictor selection function for backward selection of Cox regression ...
Predictor selection function for forward selection of Cox regression m...
Cross-validation in Multiply Imputed datasets
Cross-validation in Multiply Imputed datasets
Function for backward selection of Linear and Logistic regression mode...
Function for forward selection of Linear and Logistic regression model...
Calculates the Hosmer and Lemeshow goodness of fit test.
Kaplan-Meier risk estimates for Net Reclassification Index analysis
Kaplan-Meier (KM) estimate at specific time point
Function to calulate mean auc values
Bootstrap validation in Multiply Imputed datasets
Naive method for Cross-validation in Multiply Imputed datasets
Wrapper function around mice
External Validation of logistic prediction models in multiply imputed ...
Net Reclassification Index for Cox Regression Models
Calculation of Net Reclassification Index measures
Calculates the pooled C-statistic (Area Under the ROC Curve) across Mu...
Compare the fit and performance of prediction models across Multipy Im...
Combines the Chi Square statistics across Multiply Imputed datasets
Pools the Likelihood Ratio tests across Multiply Imputed datasets ( me...
Provides pooled adjusted intercept after shrinkage of pooled coefficie...
Pooling performance measures across multiply imputed datasets
Pooling performance measures over multiply imputed datasets
Function to pool NRI measures over Multiply Imputed datasets
Function to combine estimates by using Rubin's Rules
Pooling and Predictor selection function for backward or forward selec...
Backward selection of Cox regression models in multiply imputed data.
Forward selection of Cox regression models across multiply imputed dat...
Pooling and Predictor selection function for backward or forward selec...
Backward selection of Linear regression models across multiply imputed...
Forward selection of Linear regression models across multiply imputed ...
Pooling and Predictor selection function for backward or forward selec...
Backward selection of Logistic regression models in multiply imputed d...
Forward selection of Logistic regression models in multiply imputed da...
Pooling and Predictor selection function for multilevel models in mult...
Multiparameter pooling methods called by psfmi_mm
Internal validation and performance of logistic prediction models acro...
Function to evaluate bootstrap predictor and model stability in multip...
Internal validation and performance of logistic prediction models acro...
Risk calculation at specific time point for Cox model
Function to apply RR to pool difference of NRI and AUC values
Nagelkerke's R-square calculation for logistic regression / glm models
R-square calculation for Cox regression models
Calculates the scaled Brier score
Function to evaluate bootstrap predictor and model stability.
Pooling, backward and forward selection of linear, logistic and Cox regression models in multiply imputed datasets. Backward and forward selection can be done from the pooled model using Rubin's Rules (RR), the D1, D2, D3, D4 and the median p-values method. This is also possible for Mixed models. The models can contain continuous, dichotomous, categorical and restricted cubic spline predictors and interaction terms between all these type of predictors. The stability of the models can be evaluated using (cluster) bootstrapping. The package further contains functions to pool model performance measures as ROC/AUC, Reclassification, R-squared, scaled Brier score, H&L test and calibration plots for logistic regression models. Internal validation can be done across multiply imputed datasets with cross-validation or bootstrapping. The adjusted intercept after shrinkage of pooled regression coefficients can be obtained. Backward and forward selection as part of internal validation is possible. A function to externally validate logistic prediction models in multiple imputed datasets is available and a function to compare models. For Cox models a strata variable can be included. Eekhout (2017) <doi:10.1186/s12874-017-0404-7>. Wiel (2009) <doi:10.1093/biostatistics/kxp011>. Marshall (2009) <doi:10.1186/1471-2288-9-57>.