Clinical Prediction Models: A Practical Approach to Development, Validation, and Updating

Preface viiAcknowledgements xiChapter 1 Introduction 11.1 Diagnosis, prognosis and therapy choice in medicine 11.1.1 Predictions for personalized evidence-based medicine 11.2 Statistical modeling for prediction 51.2.1 Model assumptions 51.2.2 Reliability of predictions: aleatory and epistemic uncertainty 61.2.3 Sample size 61.3 Structure of the book 81.3.1 Part I: Prediction models in medicine 81.3.2 Part II: Developing internally valid prediction models 81.3.3 Part III: Generalizability of prediction models 91.3.4 Part IV: Applications 9Part I: Prediction models in medicine 11Chapter 2 Applications of prediction models 132.1 Applications: medical practice and research 132.2 Prediction models for Public Health 142.2.1 Targeting of preventive interventions 14*2.2.2 Example: prediction for breast cancer 142.3 Prediction models for clinical practice 172.3.1 Decision support on test ordering 17*2.3.2 Example: predicting renal artery stenosis 172.3.3 Starting treatment: the treatment threshold 20*2.3.4 Example: probability of deep venous thrombosis 202.3.5 Intensity of treatment 21*2.3.6 Example: defining a poor prognosis subgroup in cancer 222.3.7 Cost-effectiveness of treatment 232.3.8 Delaying treatment 23*2.3.9 Example: spontaneous pregnancy chances 242.3.10 Surgical decision-making 26*2.3.11 Example: replacement of risky heart valves 272.4 Prediction models for medical research 282.4.1 Inclusion and stratification in a RCT 28*2.4.2 Example: selection for TBI trials 292.4.3 Covariate adjustment in a RCT 302.4.4 Gain in power by covariate adjustment 31*2.4.5 Example: analysis of the GUSTO-III trial 322.4.6 Prediction models and observational studies 322.4.7 Propensity scores 33*2.4.8 Example: statin treatment effects 342.4.9 Provider comparisons 35*2.4.10 Example: ranking cardiac outcome 352.5 Concluding remarks 35Chapter 3 Study design for prediction modeling 373.1 Studies for prognosis 373.1.1 Retrospective designs 37*3.1.2 Example: predicting early mortality in esophageal cancer 373.1.3 Prospective designs 38*3.1.4 Example: predicting long-term mortality in esophageal cancer 393.1.5 Registry data 39*3.1.6 Example: surgical mortality in esophageal cancer 393.1.7 Nested case-control studies 40*3.1.8 Example: perioperative mortality in major vascular surgery 403.2 Studies for diagnosis 413.2.1 Cross-sectional study design and multivariable modeling 41*3.2.2 Example: diagnosing renal artery stenosis 413.2.3 Case-control studies 41*3.2.4 Example: diagnosing acute appendicitis 423.3 Predictors and outcome 423.3.1 Strength of predictors 423.3.2 Categories of predictors 423.3.3 Costs of predictors 433.3.4 Determinants of prognosis 443.3.5 Prognosis in oncology 443.4 Reliability of predictors 453.4.1 Observer variability 45*3.4.2 Example: histology in Barrett's esophagus 453.4.3 Biological variability 463.4.4 Regression dilution bias 46*3.4.5 Example: simulation study on reliability of a binary predictor 463.4.6 Choice of predictors 473.5 Outcome 473.5.1 Types of outcome 473.5.2 Survival endpoints 48*3.5.3 Examples: 5-year relative survival in cancer registries 483.5.4 Composite endpoints 49*3.5.5 Example: composite endpoints in cardiology 49

Ewout Steyerberg worked for 25 years at Erasmus Medical Center in Rotterdam before moving to Leiden where he is now Professor of Clinical Biostatistics and Medical Decision Making and chair of the Department of Biomedical Data Sciences at Leiden University Medical Center. His research has covered a broad range of methodological and medical topics, which is reflected in hundreds of peer-reviewed methodological and applied publications. His methodological expertise is in the design and analysis of randomized controlled trials, cost-effectiveness analysis, and decision analysis. His methodological research focuses on the development, validation and updating of prediction models, as reflected in a textbook (Springer, 2009). His medical fields of application include oncology, cardiovascular disease, internal medicine, pediatrics, infectious diseases, neurology, surgery and traumatic brain injury.