Medical Risk Prediction Models : With Ties to Machine Learning (Hardcover)

Software Why should I care about statistical prediction models? The many uses of prediction models in medicine The unique messages of this book Prognostic factor modeling philosophy The rest of this book I am going to make a prediction model What do I need to know? Prediction model framework Target population The time origin The event of interest The prediction time horizon and follow-up Landmarking Risks and risk predictions Classification of risk Predictor variables Checklist Prediction performance Proper scoring rules Calibration Discrimination Explained variation Variability and uncertainty The interpretation is relative Utility Average versus subgroups Study design Study design and sources of information Cohort Multi-center study Randomized clinical trial Case-control Given treatment and treatment options Sample size calculation Data Purpose dataset Data dictionary Measurement error Missing values Censored data Competing risks Modeling Risk prediction model Risk classifier How is prediction modeling different from statistical inference? Regression model Linear predictor Expert selects the candidate predictors How to select variables for inclusion in the final model All possible interactions Checklist Machine learning Validation The conventional model Internal and external validation Conditional versus expected performance Cross-validation Data splitting Bootstrap Model checking and goodness of fit Reproducibility Pitfalls Age as time scale Odds ratios and hazard ratios are not predictions of risks Do not blame the metric Censored data versus competing risks Disease-specific survival Overfitting Data-dependent decisions Balancing data Independent predictor Automated variable selection How should I prepare for modeling? Definition of subjects Choice of time scale Pre-selection of predictor variables Preparation of predictor variables Categorical variables Continuous variables Derived predictor variables Repeated measurements Measurement error Missing values Preparation of event time outcome Illustration without competing risks Illustration with competing risks Artificial censoring at the prediction time horizon I am ready to build a prediction model Specifying the model type Uncensored binary outcome Right-censored time-to-event outcome (no competing risks) Right-censored time-to-event outcome with competing risks Benchmark model Uncensored binary outcome Right-censored time-to-event outcome (without competing risks) Right-censored time-to-event with competing risks Including predictor variables Categorical predictor variables Continuous predictor variables Interaction effects Modeling strategy Variable selection Conventional model strategy Whether to use a standard regression model or something else Advanced topics How to prevent overfitting the data How to deal with missing values How to deal with non-converging models What you should put in your manuscript Baseline tables Follow Up tables Regression tables Risk plots Nomograms Deployment Risk charts Internet calculator Cost-benefit analysis (waiting lists) Does my model predict accurately? Model assessment roadmap Visualization of the predictions Calculation of model performance Visualization of model performance Uncensored binary outcome Distribution of the predicted risks Brier score AUC Calibration curves Right-censored time-to-event outcome (without competing risks) Distribution of the predicted risks Brier score with censored data Time-dependent AUC for censored data Calibration curve for censored data Competing risks Distribution of the predicted risks Brier score with competing risks Time-dependent AUC for competing risks Calibration curve for competing risks The Index of Prediction Accuracy (IPA) Choice of prediction time horizon Time-dependent prediction performance How do I decide between rival models? Model comparison roadmap Analysis of rival prediction models Uncensored binary outcome Right-censored time-to-event outcome (without competing risks) Competing risks Clinically relevant change of prediction Does a new marker improve prediction? Many new predictors Updating a subject's prediction What would make me an expert? Multiple cohorts / Multi-center studies The role of treatment for making a prediction model Modeling treatment Comparative effectiveness tables Learning curve paradigm Internal validation (data splitting) Single split Calendar split Multiple splits (cross-validation) Dilemma of internal validation The apparent and the + estimator Tips and tricks Missing values Missing values in the learning data Missing values in the validation data Time-varying coefficient models Time-varying predictor variables Can't the computer just take care of all of this? Zero layers of cross-validation What may happen if you do not look at the data Unsupervised modeling steps Final model One layer of cross-validation Penalized regression Supervised spline selection Machine learning (two levels of cross-validation) Random forest Deep learning and artificial neural networks The super learner Things you might have expected in our book Threshold selection for decision making Number of events per variable Confidence intervals for predicted probabilities Models developed from case-control data Hosmer-Lemeshow test Backward elimination and stepwise selection Rank correlation (c-index) for survival outcome Integrated Brier score Net reclassification index and the integrated discrimination improvement Re-classification tables Boxplots of rival models conditional on the outcome