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Clinical Prediction Models : A Practical Approach to Development, Validation, and Updating
Clinical Prediction Models : A Practical Approach to Development, Validation, and Updating
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Author(s): Steyerberg, Ewout W.
ISBN No.: 9783030163983
Pages: xxxiii, 562
Year: 201908
Format: Trade Cloth (Hard Cover)
Price: £71.18
Dispatch delay: Dispatched between 7 to 15 days
Status: Available (Forthcoming)

Preface vii Acknowledgements xi Chapter 1 Introduction 1 1.1 Diagnosis, prognosis and therapy choice in medicine 1 1.1.1 Predictions for personalized evidence-based medicine 1 1.2 Statistical modeling for prediction 5 1.2.1 Model assumptions 5 1.2.


2 Reliability of predictions: aleatory and epistemic uncertainty 6 1.2.3 Sample size 6 1.3 Structure of the book 8 1.3.1 Part I: Prediction models in medicine 8 1.3.2 Part II: Developing internally valid prediction models 8 1.


3.3 Part III: Generalizability of prediction models 9 1.3.4 Part IV: Applications 9 Part I: Prediction models in medicine 11 Chapter 2 Applications of prediction models 13 2.1 Applications: medical practice and research 13 2.2 Prediction models for Public Health 14 2.2.1 Targeting of preventive interventions 14 *2.


2.2 Example: prediction for breast cancer 14 2.3 Prediction models for clinical practice 17 2.3.1 Decision support on test ordering 17 *2.3.2 Example: predicting renal artery stenosis 17 2.3.


3 Starting treatment: the treatment threshold 20 *2.3.4 Example: probability of deep venous thrombosis 20 2.3.5 Intensity of treatment 21 *2.3.6 Example: defining a poor prognosis subgroup in cancer 22 2.3.


7 Cost-effectiveness of treatment 23 2.3.8 Delaying treatment 23 *2.3.9 Example: spontaneous pregnancy chances 24 2.3.10 Surgical decision-making 26 *2.3.


11 Example: replacement of risky heart valves 27 2.4 Prediction models for medical research 28 2.4.1 Inclusion and stratification in a RCT 28 *2.4.2 Example: selection for TBI trials 29 2.4.3 Covariate adjustment in a RCT 30 2.


4.4 Gain in power by covariate adjustment 31 *2.4.5 Example: analysis of the GUSTO-III trial 32 2.4.6 Prediction models and observational studies 32 2.4.7 Propensity scores 33 *2.


4.8 Example: statin treatment effects 34 2.4.9 Provider comparisons 35 *2.4.10 Example: ranking cardiac outcome 35 2.5 Concluding remarks 35 Chapter 3 Study design for prediction modeling 37 3.1 Studies for prognosis 37 3.


1.1 Retrospective designs 37 *3.1.2 Example: predicting early mortality in esophageal cancer 37 3.1.3 Prospective designs 38 *3.1.4 Example: predicting long-term mortality in esophageal cancer 39 3.


1.5 Registry data 39 *3.1.6 Example: surgical mortality in esophageal cancer 39 3.1.7 Nested case-control studies 40 *3.1.8 Example: perioperative mortality in major vascular surgery 40 3.


2 Studies for diagnosis 41 3.2.1 Cross-sectional study design and multivariable modeling 41 *3.2.2 Example: diagnosing renal artery stenosis 41 3.2.3 Case-control studies 41 *3.2.


4 Example: diagnosing acute appendicitis 42 3.3 Predictors and outcome 42 3.3.1 Strength of predictors 42 3.3.2 Categories of predictors 42 3.3.3 Costs of predictors 43 3.


3.4 Determinants of prognosis 44 3.3.5 Prognosis in oncology 44 3.4 Reliability of predictors 45 3.4.1 Observer variability 45 *3.4.


2 Example: histology in Barrett''s esophagus 45 3.4.3 Biological variability 46 3.4.4 Regression dilution bias 46 *3.4.5 Example: simulation study on reliability of a binary predictor 46 3.4.


6 Choice of predictors 47 3.5 Outcome 47 3.5.1 Types of outcome 47 3.5.2 Survival endpoints 48 *3.5.3 Examples: 5-year relative survival in cancer registries 48 3.


5.4 Composite endpoints 49 *3.5.5 Example: composite endpoints in cardiology 49 3.5.6 Choice of prognostic outcome 49 3.5.7 Diagnostic endpoints 49 *3.


5.8 Example: PET scans in esophageal cancer 50 3.6 Phases of biomarker development 50 3.7 Statistical power and reliable estimation 51 3.7.1 Sample size to identify predictor effects 51 3.7.2 Sample size for reliable modeling 53 3.


7.3 Sample size for reliable validation 55 3.8 Concluding remarks 55 Chapter 4 Statistical models for prediction 57 4.1 Continuous outcomes 57 *4.1.1 Examples of linear regression 58 4.1.2 Economic outcomes 58 *4.


1.3 Example: prediction of costs 58 4.1.4 Transforming the outcome 58 4.1.5 Performance: explained variation 59 4.1.6 More flexible approaches 60 4.


2 Binary outcomes 61 4.2.1 R2 in logistic regression analysis 62 4.2.2 Calculation of R2 on the log likelihood scale 63 4.2.3 Models related to logistic regression 65 4.2.


4 Bayes rule 65 4.2.5 Prediction with Naïve Bayes 66 4.2.6 Calibration and Naïve Bayes 67 *4.2.7 Logistic regression and Bayes 67 4.2.


8 Machine learning: more flexible approaches 68 4.2.9 Classification and regression trees 69 *4.2.10 Example: mortality in acute MI patients 69 4.2.11 Advantages and disadvantages of tree models 70 4.2.


12 Trees versus logistic regression modeling 70 *4.2.13 Other methods for binary outcomes 71 4.2.14 Summary on binary outcomes 72 4.3 Categorical outcomes 73 4.3.1 Polytomous logistic regression 73 4.


3.2 Example: histology of residual masses 73 *4.3.3 Alternative models 75 *4.3.4 Comparison of modeling approaches 76 4.4 Ordinal outcomes 77 4.4.


1 Proportional odds logistic regression 77 * 4.4.2 Relevance of the proportional odds assumption in RCTs 78 4.5 Survival outcomes 80 4.5.1 Cox proportional hazards regression 80 4.5.2 Prediction with Cox models 81 4.


5.3 Proportionality assumption 81 4.5.4 Kaplan-Meier analysis 81 *4.5.5 Example: impairment after treatment of leprosy 82 4.5.6 Parametric survival 82 *4.


5.7 Example: replacement of risky heart valves 83 4.5.8 Summary on survival outcomes 83 4.6 Competing risks 84 4.6.1 Actuarial and actual risks 84 4.6.


2 Absolute risk and the Fine&Gray model 84 4.6.3 Example: Prediction of coronary heart disease incidence 85 4.6.4 Multi-state modeling 86 4.7 Dynamic predictions 87 4.7.1 Multi-state models and landmarking 87 4.


7.2 Joint models 87 4.8 Concluding remarks 88 Chapter 5 Overfitting and optimism in prediction models 91 5.1 Overfitting and optimism 91 5.1.1 Example: surgical mortality in esophagectomy 92 5.1.2 Variability within one center 92 5.


1.3 Variability between centers: noise vs. true heterogeneity 93 5.1.4 Predicting mortality by center: shrinkage 94 5.2 Overfitting in regression models 95 5.2.1 Model uncertainty and testimation bias 95 5.


2.2 Other modeling biases 97 5.2.3 Overfitting by parameter uncertainty 97 5.2.4 Optimism in model performance 98 5.2.5 Optimism-corrected performance 99 5.


3 Bootstrap resampling 100 5.3.1 Applications of the bootstrap 101 5.3.2 Bootstrapping for regression coefficients 102 5.3.3 Bootstrapping for prediction: optimism correction 102 5.3.


4 Calculation of optimism-corrected performance 103 *5.3.5 Example: Stepwise selection in 429 patients 104 5.4 Cost of data analysis 105 *5.4.1 Degrees of freedom of a model 105 5.4.2 Practical implications 105 5.


5 Concluding remarks 106 Chapter 6 Choosing between alternative models 109 6.1 Prediction with statistical models 109 6.1.1 Testing of model assumptions and prediction 110 6.1.2 Choosing a type of model 110 6.2 Modeling age - outcome relations 111 *6.2.


1 Age and mortality after acute MI 111 *6.2.2 Age and operative mortality 112 *6.2.3 Age - outcome relations in other diseases 115 6.3 Head-to-head comparisons 116 6.3.1 StatLog results 116 *6.


3.2 Cardiovascular disease prediction comparisons 117 *6.3.3 Traumatic brain injury modeling results 119 6.4 Concluding remarks 120 Part II: Developing valid prediction models 123 Checklist for developing valid prediction models 124 Chapter 7 Missing values 125 7.1 Missing values and prediction research 125 7.1.1 Inefficiency of complete case analysis 126 7.


1.2 Interpretation of CC Analyses 127 7.1.3 Missing data mechanisms 127 7.1.4 Missing outcome data 128 7.1.5 Summary points 129 7.


2 Prediction under MCAR, MAR and MNAR mechanisms 130 7.2.1 Missingness patterns 130 7.2.2 Missingness and estimated regression coefficients 132 7.2.4 Missingness and estimated performance 134 7.3 Dealing with missing values in regression analysis 135 7.


3.1 Imputation principle 135 7.3.2 Simple and more advanced single imputation methods 136 7.3.3 Multiple imputation 137 7.4 Defining the imputation model 138 7.4.


1 Types of variables in the imputation model 138 *7.4.2 Transformations of variables 139 7.4.3 Imputation mode.


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