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Medical Risk Prediction Models

With Ties to Machine Learning

; Michael W. Kattan

«<p>"Two of the top researchers in the field of clinical prediction models have produced a highly innovative book that brings a very technical topic to public grasp by throwing out the formulas and just talking straight from the heart of practical experience. While clinicians and medical residents can now learn how to build, diagnose and validate risk models themselves, all public health researchers, old and new, will reap the benefits and enjoyment from reading this book."<br /><i>~Donna Ankerst, Technical University of Munich</i></p> <p></p>»

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. Les mer
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Legg i
Vår pris: 1012,-

(Innbundet) Fri frakt!
Leveringstid: Sendes innen 7 virkedager

Om boka

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.

Fakta

Innholdsfortegnelse

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

Om forfatteren

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.