Medical Risk Prediction Models With Ties to Machine Learning

Medical Risk Prediction Models: With Ties to Machine Learning is a hands-on book for clinicians, epidemiologists, and professional statisticians who need to make or evaluate a statistical prediction model based on data. The subject of the book is the patient’s individualized probability of a medical event within a given time horizon. Gerds and Kattan describe the mathematical details of making and evaluating a statistical prediction model in a highly pedagogical manner while avoiding mathematical notation. Read this book when you are in doubt about whether a Cox regression model predicts better than a random survival forest. Features: All you need to know to correctly make an online risk calculator from scratch Discrimination, calibration, and predictive performance with censored data and competing risks R-code and illustrative examples Interpretation of prediction performance via benchmarks Comparison and combination of rival modeling strategies via cross-validation Thomas A. Gerds is a professor at the Biostatistics Unit at the University of Copenhagen and is affiliated with the Danish Heart Foundation. He is the author of several R-packages on CRAN and has taught statistics courses to non-statisticians for many years. Michael W. Kattan is a highly cited author and Chair of the Department of Quantitative Health Sciences at Cleveland Clinic. He is a Fellow of the American Statistical Association and has received two awards from the Society for Medical Decision Making: the Eugene L. Saenger Award for Distinguished Service, and the John M. Eisenberg Award for Practical Application of Medical Decision-Making Research.

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

Thomas A. Gerds is professor at the biostatistics unit at the University of Copenhagen. He is affiliated with the Danish Heart Foundation. He is author of several R-packages on CRAN and has taught statistics courses to non-statisticians for many years. Michael Kattan is a highly cited author and Chair of the Department of Quantitative Health Sciences at Cleveland Clinic. He is a Fellow of the American Statistical Association and has received two awards from the Society for Medical Decision Making: the Eugene L. Saenger Award for Distinguished Service, and the John M. Eisenberg Award for Practical Application of Medical Decision Making Research.