Clinical prediction models : a practical approach to development, validation, and updating /: a practical approach to development, validation, and updating. (2019)

• Record Type: Book
• Further Information: Note: Ewout W. Steyerberg.
• Other Names: Steyerberg, Ewout W
• Contents: 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 493.5.6 Choice of prognostic outcome 493.5.7 Diagnostic endpoints 49*3.5.8 Example: PET scans in esophageal cancer 503.6 Phases of biomarker development 503.7 Statistical power and reliable estimation 513.7.1 Sample size to identify predictor effects 513.7.2 Sample size for reliable modeling 533.7.3 Sample size for reliable validation 553.8 Concluding remarks 55Chapter 4 Statistical models for prediction 574.1 Continuous outcomes 57*4.1.1 Examples of linear regression 584.1.2 Economic outcomes 58*4.1.3 Example: prediction of costs 584.1.4 Transforming the outcome 584.1.5 Performance: explained variation 594.1.6 More flexible approaches 604.2 Binary outcomes 614.2.1 R2 in logistic regression analysis 624.2.2 Calculation of R2 on the log likelihood scale 634.2.3 Models related to logistic regression 654.2.4 Bayes rule 654.2.5 Prediction with Naïve Bayes 664.2.6 Calibration and Naïve Bayes 67*4.2.7 Logistic regression and Bayes 674.2.8 Machine learning: more flexible approaches 684.2.9 Classification and regression trees 69*4.2.10 Example: mortality in acute MI patients 694.2.11 Advantages and disadvantages of tree models 704.2.12 Trees versus logistic regression modeling 70*4.2.13 Other methods for binary outcomes 714.2.14 Summary on binary outcomes 724.3 Categorical outcomes 734.3.1 Polytomous logistic regression 734.3.2 Example: histology of residual masses 73*4.3.3 Alternative models 75*4.3.4 Comparison of modeling approaches 764.4 Ordinal outcomes 774.4.1 Proportional odds logistic regression 77* 4.4.2 Relevance of the proportional odds assumption in RCTs 784.5 Survival outcomes 804.5.1 Cox proportional hazards regression 804.5.2 Prediction with Cox models 814.5.3 Proportionality assumption 814.5.4 Kaplan-Meier analysis 81*4.5.5 Example: impairment after treatment of leprosy 824.5.6 Parametric survival 82*4.5.7 Example: replacement of risky heart valves 834.5.8 Summary on survival outcomes 834.6 Competing risks 844.6.1 Actuarial and actual risks 844.6.2 Absolute risk and the Fine&Gray model 844.6.3 Example: Prediction of coronary heart disease incidence 854.6.4 Multi-state modeling 864.7 Dynamic predictions 874.7.1 Multi-state models and landmarking 874.7.2 Joint models 874.8 Concluding remarks 88Chapter 5 Overfitting and optimism in prediction models 915.1 Overfitting and optimism 915.1.1 Example: surgical mortality in esophagectomy 925.1.2 Variability within one center 925.1.3 Variability between centers: noise vs. true heterogeneity 935.1.4 Predicting mortality by center: shrinkage 945.2 Overfitting in regression models 955.2.1 Model uncertainty and testimation bias 955.2.2 Other modeling biases 975.2.3 Overfitting by parameter uncertainty 975.2.4 Optimism in model performance 985.2.5 Optimism-corrected performance 995.3 Bootstrap resampling 1005.3.1 Applications of the bootstrap 1015.3.2 Bootstrapping for regression coefficients 1025.3.3 Bootstrapping for prediction: optimism correction 1025.3.4 Calculation of optimism-corrected performance 103*5.3.5 Example: Stepwise selection in 429 patients 1045.4 Cost of data analysis 105*5.4.1 Degrees of freedom of a model 1055.4.2 Practical implications 1055.5 Concluding remarks 106Chapter 6 Choosing between alternative models 1096.1 Prediction with statistical models 1096.1.1 Testing of model assumptions and prediction 1106.1.2 Choosing a type of model 1106.2 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 1156.3 Head-to-head comparisons 1166.3.1 StatLog results 116*6.3.2 Cardiovascular disease prediction comparisons 117*6.3.3 Traumatic brain injury modeling results 1196.4 Concluding remarks 120Part II: Developing valid prediction models 123Checklist for developing valid prediction models 124Chapter 7 Missing values 1257.1 Missing values and prediction research 1257.1.1 Inefficiency of complete case analysis 1267.1.2 Interpretation of CC Analyses 1277.1.3 Missing data mechanisms 1277.1.4 Missing outcome data 1287.1.5 Summary points 1297.2 Prediction under MCAR, MAR and MNAR mechanisms 1307.2.1 Missingness patterns 1307.2.2 Missingness and estimated regression coefficients 1327.2.4 Missingness and estimated performance 1347.3 Dealing with missing values in regression analysis 1357.3.1 Imputation principle 1357.3.2 Simple and more advanced single imputation methods 1367.3.3 Multiple imputation 1377.4 Defining the imputation model 1387.4.1 Types of variables in the imputation model 138*7.4.2 Transformations of variables 1397.4.3 Imputation models for SI 1397.4.4 Summary points 1397.5 Success of imputation under MCAR, MAR and MNAR 1407.5.1 Imputation in a simple model 1407.5.2 Other simulation results 140* 7.5.3 Multiple predictors 1407.6 Guidance to dealing with missing values in prediction research 1427.6.1 Patterns of missingness 1427.6.2 Simple approaches 1437.6.3 More advanced approaches 1437.6.4 Maximum fraction of missing values before omitting a predictor 1437.6.5 Single or multiple imputation for predictor effects? 1447.6.6 Single or multiple imputation for deriving predictions? 1457.6.7 Missings and predictions for new patients 145*7.6.8 Performance across multiple imputed data sets 1467.6.9 Reporting of missing values in prediction research 1467.7 Concluding remarks 1487.7.1 Summary statements 148*7.7.2 Available software and challenges 149Chapter 8 Case study on dealing with missing values 1518.1 Introduction 1518.1.1 Aim of the IMPACT study 1518.1.2 Patient selection 1528.1.3 Potential predictors 1528.1.4 Coding and time dependency of predictors 1538.2 Missing values in the IMPACT study 1538.2.1 Missing values in outcome 1538.2.2 Quantification of missingness of predictors 1548.2.3 Patterns of missingness 1568.3 Imputation of missing predictor values 1598.3.1 Correlations between predictors 1598.3.2 Imputation model 1608.3.3 Distributions of imputed values 160*8.3.4 Multilevel imputation 1618.4 Predictor effect: adjusted analyses 1628.4.1 Adjusted analysis for complete predictors: age and motor score 1638.4.2 Adjusted analysis for incomplete predictors: pupils 1658.5 Predictions: multivariable analyses 165*8.5.1 Multilevel analyses 1668.6 Concluding remarks 166Chapter 9 Coding of categorical and continuous predictors 1699.1 Categorical predictors 1699.1.1 Examples of categorical coding 1709.2 Continuous predictors 171*9.2.1 Examples of continuous predictors 1719.2.2 Categorization of continuous predictors 1729.3 Non-linear functions for continuous predictors 1739.3.1. Polynomials 1739.3.2. Fractional polynomials (FP) 1749.3.3 Splines 175*9.3.4 Example: functional forms with RCS or FP 1769.3.5 Extrapolation and robustness 1769.3.5 Preference for FP or RCS? 1769.4 Outliers and winsorizing ......
• Edition: 2nd ed
• Publisher Details: Cham : Springer
• Publication Date: 2019
• Copyright Date: 2019
• Extent: 1 online resource (558 pages)
• Subjects: 610.727; Medical statistics; Medicine -- Research -- Statistical methods; Evidence-based medicine -- Statistical methods; Clinical trials -- Statistical methods; Regression analysis
• Languages: English
• ISBNs: 9783030163990
• Related ISBNs: 9783030163983
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