Clinical prediction models: a practical approach to development, validation, and updating
Gespeichert in:
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| Format: | Buch |
| Sprache: | English |
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New York, NY
Springer
2009
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| Schriftenreihe: | Statistics for biology and health
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| Schlagworte: | |
| Online-Zugang: | Inhaltsverzeichnis |
| Beschreibung: | XXVIII, 497 S. |
| ISBN: | 038777243X 9780387772431 |
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MARC
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| 100 | 1 | |a Steyerberg, Ewout W. |d 1967- |e Verfasser |0 (DE-588)1057801720 |4 aut | |
| 245 | 1 | 0 | |a Clinical prediction models |b a practical approach to development, validation, and updating |c Ewout W. Steyerberg |
| 264 | 1 | |a New York, NY |b Springer |c 2009 | |
| 300 | |a XXVIII, 497 S. | ||
| 336 | |b txt |2 rdacontent | ||
| 337 | |b n |2 rdamedia | ||
| 338 | |b nc |2 rdacarrier | ||
| 490 | 0 | |a Statistics for biology and health | |
| 650 | 7 | |a prediction |2 cabt | |
| 650 | 7 | |a statistics |2 cabt | |
| 650 | 7 | |a data analysis |2 cabt | |
| 650 | 7 | |a data processing |2 cabt | |
| 650 | 7 | |a mathematical models |2 cabt | |
| 650 | 7 | |a simulation models |2 cabt | |
| 650 | 7 | |a epidemiology |2 cabt | |
| 650 | 2 | |a Analyse de régression | |
| 650 | 7 | |a Médecine - Recherche - Statistiques |2 ram | |
| 650 | 2 | |a Médecine fondée sur la preuve - statistiques et données numériques | |
| 650 | 7 | |a Statistiques médicales |2 ram | |
| 650 | 7 | |a Études cliniques - Statistiques |2 ram | |
| 650 | 4 | |a Medizin | |
| 650 | 4 | |a Clinical trials |x Statistical methods | |
| 650 | 4 | |a Evidence-based medicine |x Statistical methods | |
| 650 | 4 | |a Medical statistics | |
| 650 | 4 | |a Medicine |x Research |x Statistical methods | |
| 650 | 4 | |a Models, Statistical | |
| 650 | 4 | |a Prognosis | |
| 650 | 4 | |a Regression Analysis | |
| 650 | 4 | |a Regression analysis | |
| 650 | 0 | 7 | |a Medizinische Statistik |0 (DE-588)4127563-9 |2 gnd |9 rswk-swf |
| 689 | 0 | 0 | |a Medizinische Statistik |0 (DE-588)4127563-9 |D s |
| 689 | 0 | |5 DE-604 | |
| 776 | 0 | 8 | |i Erscheint auch als |n Online-Ausgabe |z 978-0-387-77244-8 |
| 856 | 4 | 2 | |m Digitalisierung UB Regensburg |q application/pdf |u http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=017172709&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |3 Inhaltsverzeichnis |
| 999 | |a oai:aleph.bib-bvb.de:BVB01-017172709 | ||
Datensatz im Suchindex
| _version_ | 1804138696157429760 |
|---|---|
| adam_text | Contents
Preface
.......................................................
vii
Acknowledgements
............................................ xi
1
Introduction
............................................... 1
1.1
Prognosis and Prediction in Medicine
....................... 1
1.1.1
Prediction Models and Decision-Making
.............. 1
1.2
Statistical Modelling for Prediction
......................... 2
1.2.1
Model Uncertainty
............................... 3
1.2.2
Sample Size
..................................... 4
1.3
Structure of the Book
.................................... 5
1.3.1
Part I: Prediction Models in Medicine
................ 5
1.3.2
Part II: Developing Valid Prediction Models
........... 6
1.3.3
Part III: Generalizability of Prediction Models
......... 6
1.3.4
Part IV: Applications
.............................. 7
1.3.5
Questions and Exercises
........................... 7
Part I Prediction Models in Medicine
2
Applications of Prediction Models
............................. 9
2.1
Applications: Medical Practice and Research
.................. 11
2.2
Prediction Models for Public Health
......................... 12
2.2.1
Targeting of Preventive Interventions
................. 12
2.2.2
Example: Incidence of Breast Cancer
................. 12
2.3
Prediction Models for Clinical Practice
...................... 13
2.3.1
Decision Support on Test Ordering
................... 13
2.3.2
Example: Predicting Renal Artery Stenosis
............ 14
2.3.3
Starting Treatment: the Treatment Threshold
........... 15
2.3.4
Example: Probability of Deep Venous Thrombosis
...... 16
2.3.5
Intensity of Treatment
............................. 16
2.3.6
Example: Defining a Poor Prognosis Subgroup
in Cancer
....................................... 18
xjv
Contents
2.3.7
Cost-Effectiveness
of Treatment
..................... 18
2.3.8
Delaying Treatment
............................... 19
2.3.9
Example: Spontaneous Pregnancy Chances
............ 19
2.3.10
Surgical Decision-Making
......................... 21
2.3.11
Example: Replacement of Risky Heart Valves
.......... 21
2.4
Prediction Models for Medical Research
..................... 23
2.4.1
Inclusion and Stratification in an RCT
................ 23
2.4.2
Example: Selection for TBI Trials
................... 24
2.4.3
Covariate Adjustment in an RCT
.................... 25
2.4.4
Gain in Power by Covariate Adjustment
.............. 26
*2.4.5 Example: Analysis of the GUSTO-III Trial
............ 27
2.4.6
Prediction Models and Observational Studies
.......... 27
2.4.7
Propensity Scores
................................ 28
2.4.8
Example: Statin Treatment Effects
................... 28
2.4.9
Provider Profiling
................................ 29
2.4.10
Example: Ranking Cardiac Outcome
................. 29
2.5
Concluding Remarks
..................................... 30
3
Study Design for Prediction Models
............................ 33
3.1
Study Design
........................................... 33
3.2
Cohort Studies for Prognosis
.............................. 33
3.2.
1 Retrospective Designs
............................. 35
3.2.2
Example: Predicting Early Mortality in Oesophageal
Cancer
......................................... 35
3.2.3
Prospective Designs
............................35
3.2.4
Example: Predicting Long-Term Mortality in Oesophageal
Cancer
......................................... 36
3.2.5
Registry Data
................................... 36
3.2.6
Example: Surgical Mortality in Oesophageal Cancer
..... 37
3.2.7
Nested Case-Control Studies
....................... 37
3.2.8
Example:
Perioperative
Mortality in Major
Vascular Surgery
................................. 38
3.3
Studies for Diagnosis
..................................38
3.3.1
Cross-Sectional Study Design and
Multivariable
Modelling
38
3.3.2
Example: Diagnosing Renal Artery Stenosis
........... 38
3.3.3
Case-Control Studies
............................. 39
3.3.4
Example: Diagnosing Acute Appendicitis
............. 39
3.4
Predictors and Outcome
.................................. 39
3.4.1
Strength of Predictors
............................. 39
3.4.2
Categories of Predictors
........................... 40
3.4.3
Costs of Predictors
............................... 40
3.4.4
Determinants of Prognosis
......................... 41
3.4.5
Prognosis in Oncology
............................ 41
Contents xv
3.5
Reliability of Predictors
.................................. 42
3.5.1
Observer Variability
.............................. 42
3.5.2
Example: Histology in Barrett s Oesophagus
........... 42
3.5.3
Biological Variability
............................. 43
3.5.4
Regression Dilution Bias
.......................... 43
3.5.5
Example: Simulation Study on Reliability of a
Binary Predictor
................................. 43
3.5.6
Choice of Predictors
.............................. 44
3.6
Outcome
.............................................. 44
3.6.1
Types of Outcome
................................ 44
3.6.2
Survival
Endpoints............................... 45
3.6.3
Example: Relative Survival in Cancer Registries
........ 45
3.6.4
Composite End Points
............................. 46
3.6.5
Example: Mortality and Composite End Points
in Cardiology
................................... 46
3.6.6
Choice of Prognostic Outcome
...................... 46
3.6.7
Diagnostic End Points
............................. 47
3.6.8
Example: PET Scans in Oesophageal Cancer
........... 47
3.7
Phases of
Biomarker
Development
.......................... 47
3.8
Statistical Power
........................................ 48
3.8.1
Statistical Power to Identify Predictor Effects
.......... 49
3.8.2
Examples of Statistical Power Calculations
............ 49
3.8.3
Statistical Power for Reliable Predictions
.............. 50
3.9
Concluding Remarks
..................................... 51
4
Statistical Models for Prediction
.............................. 53
4.1
Continuous Outcomes
.................................... 53
4.1.1
Examples of Linear Regression
..................... 54
4.1.2
Economic Outcomes
.............................. 54
4.1.3
Example: Prediction of Costs
....................... 54
4.1.4
Transforming the Outcome
......................... 54
4.1.5
Performance: Explained Variation
................... 55
4.1.6
More Flexible Approaches
......................... 55
4.2
Binary Outcomes
....................................... 57
4.2.1
R2 in Logistic Regression Analysis
................... 58
4.2.2
Calculation of R2 on the Log Likelihood Scale
.......... 58
4.2.3
Models Related to Logistic Regression
............... 60
4.2.4
Bayes
Rule
..................................... 61
4.2.5
Example: Calculations with Likelihood Ratios
......... 62
4.2.6
Prediction with
Naïve Bayes........................
63
4.2.7
Examples of
Naïve Bayes..........................
65
4.2.8
Calibration and
Naïve Bayes
....................... 65
4.2.9
Logistic Regression and
Bayes
...................... 65
4.2.10
More Flexible Approaches to Binary Outcomes
........ 65
xv¿
Contents
4.2.11
Classification and Regression Trees
.................. 67
4.2.12
Example: Mortality in Acute MI Patients
.............. 67
4.2.13
Advantages and Disadvantages of Tree Models
......... 67
4.2.14
Trees as Special Cases of Logistic Regression
Modelling
...................................... 69
4.2.14
Other Methods for Binary Outcomes
................. 70
4.2.15
Summary on Binary Outcomes
...................... 71
4.3
Categorical Outcomes
.................................... 71
4.3.1
Polytomous Logistic Regression
..................... 72
4.3.2
Example: Histology of Residual Masses
.............. 72
4.3.3
Alternative Models
............................... 73
4.3.4
Comparison of Modelling Approaches
................ 74
4.4
Ordinal Outcomes
....................................... 74
4.4.1
Proportional Odds Logistic Regression
............... 75
4.4.2
Alternative: Continuation Ratio Model
................ 77
4.5
Survival Outcomes
...................................... 77
4.5.1
Cox Proportional Hazards Regression
................ 77
4.5.2
Predicting with Cox
.............................. 78
4.5.3
Proportionality Assumption
........................ 78
4.5.4
Kaplan-Meier Analysis
........................... 79
4.5.5
Example: NFI After Treatment of Leprosy
............. 79
4.5.6
Parametric Survival
............................... 80
4.5.7
Example: Replacement of Risky Heart Valves
.......... 80
4.5.8
Summary on Survival Outcomes
.................... 81
4.6
Concluding Remarks
..................................... 81
5
Overfitting and Optimism in Prediction Models
.................. 83
5.1
Overfitting and Optimism
................................. 83
5.1.1
Example: Surgical Mortality in Oesophagectomy
....... 84
5.1.2
Variability within One Centre
....................... 84
5.1.3
Variability between Centres: Noise vs. True
Heterogeneity
................................... 85
5.1.4
Predicting Mortality by Centre: Shrinkage
............. 87
5.2
Overfitting in Regression Models
........................... 87
5.2.1
Model Uncertainty: Testimation
..................... 87
5.2.2
Other Biases
.................................... 89
5.2.3
Overfitting by Parameter Uncertainty
................. 90
5.2.4
Optimism in Model Performance
.................... 90
5.2.5
Optimism-Corrected Performance
................... 92
5.3
Bootstrap Resampling
................................... 92
5.3.1
Applications of the Bootstrap
....................... 93
5.3.2
Bootstrapping for Regression Coefficients
............. 93
5.3.3
Bootstrapping for Optimism Correction
............... 94
5.3.4
Calculation of Optimism-Corrected Performance
....... 95
Contents xvii
5.3.5
Example: Stepwise Selection in
429
Patients
........... 96
5.4
Cost of Data Analysis
.................................... 97
5.4.1
Example: Cost of Data Analysis in a Tree Model
....... 98
5.4.2
Practical Implications
............................. 98
5.5
Concluding Remarks
..................................... 99
6
Choosing Between Alternative Statistical Models
................. 101
6.1
Prediction with Statistical Models
.......................... 101
6.1.1
Testing of Model Assumptions and Prediction
.......... 102
6.1.2
Choosing a Type of Model
......................... 102
6.2
Modelling Age-Outcome Relationships
...................... 103
6.2.1
Age and Mortality After Acute MI
................... 103
6.2.2
Age and Operative Mortality
....................... 103
6.2.3
Age-Outcome Relationships in Other Diseases
......... 106
6.3
Head-to-Head Comparisons
............................... 107
6.3.1
StatLog Results
.................................. 107
6.3.2
GUSTO-I Modelling Comparisons
................... 108
6.3.3
GUSTO-I Results
................................ 109
6.4
Concluding Remarks
..................................... 110
Part II Developing Valid Prediction Models
7
Dealing with Missing Values
.................................. 113
7.1
Missing Values in Predictors
............................... 115
7.1.1
Inefficiency of Complete Case Analysis
............... 116
7.1.2
Interpretation of Analyses with Missing Data
.......... 117
7.1.3
Missing Data Mechanisms
......................... 117
7.1.4
Summary Points
................................. 118
7.2
Regression Coefficients Under
MC AR,
MAR, and MNAR
....... 118
7.2.1
R
Code
........................................ 120
7.3
Missing Values in Regression Analysis
...................... 121
7.3.1
Imputation Principle
.............................. 121
7.3.2
Simple and More Advanced Single Imputation
Methods
....................................... 122
7.3.3
Multiple Imputation
.............................. 123
7.4
Defining the Imputation Model
............................. 124
7.4.1
Transformations of Variables
....................... 125
7.4.2
Imputation Models for SI
.......................... 125
7.4.3
Summary Points
................................. 126
7.5
Simulations of Imputation Under MCAR, MAR, and MNAR
..... 126
7.5.1
Multiple Predictors
............................... 127
7.6
Imputation of Missing Outcomes
........................... 128
7.7
Guidance to Missing Values in Prediction Research
............ 129
xviii
СоШет
7.7.1
Patterns of Missingness
............................ 129
7.7.2
Simple Approaches
............................... 130
7.7.3
Maximum Fraction of Missing Values Before Omitting
aPredictor
...................................... 131
7.7.4
Single or Multiple Imputation for Predictor Effects?
..... 131
7.7.5
Single or Multiple Imputation for Predictions?
......... 132
7.7.6
Reporting of Missing Values in Prediction Research
..... 133
7.8
Concluding Remarks
..................................... 134
7.8.1
Summary Statements
............................. 135
7.8.2
Currently Available Software and Challenges
.......... 136
8
Case Study on Dealing with Missing Values
..................... 139
8.1
Introduction
............................................ 139
8.1.1
Aim
........................................... 139
8.1.2
Patient Selection
................................. 140
8.1.3
Selection of Potential Predictors
..................... 140
8.1.4
Coding and Time Dependency of Predictors
........... 141
8.2
Missing Values in the IMPACT Study
....................... 142
8.2.1
Missing Values in Outcome
........................ 142
8.2.2
Quantification of Missingness of Predictors
............ 143
8.2.3
Patterns of Missingness
............................ 144
8.3
Imputation of Missing Predictor Values
...................... 147
8.3.1
Correlations Between Predictors
.................... 147
8.3.2
Imputation Model
................................ 147
8.3.3
Distributions of Imputed Values
..................... 149
8.4
Estimating Adjusted Effects
............................... 149
8.4.1
Adjusted Analysis for Complete Predictors:
Age and Motor Score
............................. 151
8.4.2
Adjusted Analysis for Incomplete Predictors: Pupils
..... 154
8.5 Multivariable
Analyses
................................... 155
8.6
Concluding Remarks
..................................... 155
9
Coding of Categorical and Continuous Predictors
................ 159
9.1
Categorical Predictors
.................................... 159
9.1.1
Examples of Categorical Coding
.................... 160
9.2
Continuous Predictors
.................................... 161
9.2.1
Examples of Continuous Predictors
.................. 161
9.2.2
Categorization of Continuous Predictors
.............. 162
9.3
Non-Linear Functions for Continuous Predictors
............... 163
9.3.1
Polynomials
.................................... 164
9.3.2
Fractional Polynomials
............................ 164
9.3.3
Splines
......................................... 165
9.3.4
Example: Functional Forms with RCS or FP
........... 166
9.3.5
Extrapolation and Robustness
....................... 166
Contents
x¡x
9.4
Outliers and Truncation
.................................. 167
9.4.1
Example: Glucose Values and Outcome of TBI
......... 168
9.5
Interpretation of Effects of Continuous Predictors
.............. 170
9.5.1
Example: Predictor Effects in TBI
................... 171
9.6
Concluding Remarks
..................................... 172
9.6.1
Software
....................................... 172
10
Restrictions on Candidate Predictors
.......................... 175
10.1
Selection Before Studying the Predictor-Outcome
Relationship
......................................... 175
10.1.1
Selection Based on Subject Knowledge
............. 175
10.1.2
Example: Too Many Candidate Predictors
........... 176
10.1.3
Meta-
Analysis for Candidate Predictors
............. 176
10.1.4
Example: Predictors in Testicular Cancer
............ 176
10.1.5
Selection Based on Distributions
.................. 177
10.2
Combining Similar Variables
............................ 177
10.2.1
Example: Coding of Comorbidity
.................. 178
10.2.2
Assessing the Equal Weights Assumption
........... 178
10.2.3
Logical Weighting
.............................. 179
10.2.4
Statistical Combination
.......................... 180
10.3
Averaging Effects
..................................... 180
10.3.1
Example: Chlamydia
Trachomatis
Infection Risks
.... 180
10.3.2
Example: Acute Surgery Risk Relevant for
Elective Patients?
.............................. 180
10.4
Case study: Family History for Prediction of a
Genetic Mutation
..................................... 181
10.4.1
Clinical Background and Patient Data
.............. 181
10.4.2
Similarity of Effects
............................ 182
10.4.3 CRC
and Adenoma in
a
Proband.................. 184
10.4.4
Age of
CRC in
Family History
.................... 185
10.4.5
Full Prediction Model for Mutations
............... 186
10.5
Concluding Remarks
................................... 187
11
Selection of MainEffects
..................................... 191
11.1
Predictor Selection
.................................... 191
11.1.1
Reduction Before Modelling
...................... 191
11.1.2
Reduction While Modelling
...................... 192
11.1.3
Collinearity
................................... 192
11.1.4
Parsimony
.................................... 193
11.1.5
Should Non-Significant Variables Be Removed?
...... 193
11.1.6
Summary Points
............................... 194
1
Ì.2
Stepwise Selection
.................................... 194
11.2.1
Stepwise Selection Variants
...................... 194
11.2.2
Stopping Rules in Stepwise Selection
............... 195
Contents
XX
11.3
Advantages of Stepwise Methods
......................... 196
11.4
Disadvantages of Stepwise Methods
....................... 197
11.4.1
Instability of selection
........................... 197
11.4.2
Biased Estimation of Coefficients
.................. 199
11.4.3
Bias of Stepwise Selection and Events Per Variable
----- 199
11.4.4
Misspecifcation of Variability
..................... 201
11.4.5
Exaggeration of P-Values
........................ 204
11.4.6
Predictions of Worse Quality Than from a Full Model
. 204
11.5
Influence of Noise Variables
............................. 205
11.6
Univariate Analyses and Model Specification
............... 206
11.6.1
Pros and Cons of Univariate Pre-Selection
........... 207
11.6.2
Testing of Predictors within Domains
............... 207
11.7
Modern Selection Methods
.............................. 207
11.7.1
Bootstrapping for Selection
...................... 208
11.7.2
Bagging and Boosting
........................... 208
11.7.3
Bayesian Model Averaging
(BMA)................ 208
11.7.4
Practical Advantages of
BMA.................... 209
11.7.5
Shrinkage of Regression Coefficients to Zero
........ 210
11.8
Concluding Remarks
................................... 210
12
Assumptions in Regression Models: Additivity
and Linearity
............................................. 213
12.1
Additivity and Interaction Terms
......................... 213
12.1.1
Potential Interaction Terms to Consider
............. 214
12.1.2
Interactions with Treatment
...................... 214
12.1.3
Other Potential Interactions
...................... 215
12.1.4
Example: Time and Survival After Valve
Replacement
.................................. 216
12.2
Selection, Estimation and Performance with Interaction Terms
.. 216
12.2.1
Example: Age Interactions in GUSTO-1
............. 217
12.2.2
Estimation of Interaction Terms
................... 217
12.2.3
Better Prediction with Interaction Terms?
........... 219
12.2.4
Summary Points
............................... 220
12.3
Non-linearity in
Multivariable
Analysis
.................... 220
12.3.1 Multivariable
Restricted Cubic Splines (RCS)
........ 220
12.3.2 Multivariable
Fractional Polynomials (FP)
........... 221
12.3.3 Multivariable
Splines in GAM
.................... 222
12.4
Example: Non-Linearity in Testicular Cancer Case Study
...... 222
12.4.1
Details of
Multivariable
FP
and GAM Analyses
...... 224
12.4.2
GAM in Univariate and
Multivariable
Analysis
....... 224
12.4.3
Predictive Performance
.......................... 226
12.4.4
R
code for Non-Linear Modelling
................. 227
12.5
Concluding Remarks
................................... 227
12.5.1
Recommendations
.............................. 228
Contents xxi
13 Modern
Estimation Methods
................................. 231
13.1
Predictions from Regression and Other Models
.............. 231
13.2
Shrinkage
........................................... 232
13.2.1
Uniform Shrinkage
............................. 233
13.2.2
Uniform Shrinkage in GUSTO-
1.................. 233
13.3
Penalized Estimation
................................... 234
13.3.1
Penalized Maximum Likelihood Estimation
.......... 234
13.3.2
Penalized ML in Sample4
........................ 235
13.3.3
Shrinkage, Penalization, and Model Selection
........ 238
13.4
Lasso
............................................... 238
13.4.1
Estimation of Lasso Model
....................... 238
13.4.2
Lasso in GUSTO-I
............................. 239
13.4.3
Predictions after Shrinkage
....................... 239
13.4.4
Model Performance after Shrinkage
................ 240
13.5
Concluding Remarks
................................... 240
14
Estimation with External Information
......................... 243
14.1
Combining Literature and Individual Patient Data
............ 243
14.1.1
Adaptation Method
1........................... 244
14.1.2
Adaptation Method
2........................... 244
14.1.3
Estimation
.................................... 245
14.1.4
Simulation Results
............................. 245
14.1.5
Performance of Adapted Model
................... 247
14.1.6
Improving Calibration
........................... 247
14.2
Example: Mortality of Aneurysm Surgery
.................. 248
14.2.1
Meta-Analysis
................................. 248
14.2.2
Individual Patient Data Analysis
................... 249
14.2.3
Adaptation Results
............................. 250
14.3
Alternative Approaches
................................. 251
14.3.1
Overall Calibration
............................. 251
14.3.2
Bayesian Methods: Using Data Priors to Regression
Modelling
.................................... 251
14.3.3
Example: Predicting Neonatal Death
............... 252
14.3.4
Example: Mortality of Aneurysm Surgery
........... 252
14.4
Concluding Remarks
................................... 253
15
Evaluation of Performance
................................... 255
15.1
Overall Performance Measures
........................... 255
15.1.1
Explained Variation: R2
.......................... 255
15.1.2
Brier Score
................................... 257
15.1.3
Example: Performance of Testicular
Cancer Prediction Model
........................ 257
15.1.4
Overall Performance Measures in Survival
.......... 258
xxii Contents
15.1.5
Decomposition in Discrimination and Calibration
..... 259
15.1.6
Summary Points
............................... 259
15.2
Discriminative Ability
.................................. 260
15.2.1
Sensitivity and Specificity of Prediction Models
...... 260
15.2.2
Example: Sensitivity and Specificity of Testicular
Cancer Prediction Model
........................ 260
15.2.3
ROC Curve
................................... 260
15.2.4
R2 vs.
с
...................................... 262
15.2.5
Box Plots and Discrimination Slope
................ 264
15.2.6 Lorenz
Curve
.................................. 264
15.2.7
Discrimination in Survival Data
................... 267
15.2.8
Example: Discrimination of Testicular Cancer
Prediction Model
............................... 268
15.2.9
Verification Bias and Discriminative Ability
......... 269
15.2.10
R
Code
...................................... 269
15.3
Calibration
........................................... 270
15.3.1
Calibration Plot
................................ 270
15.3.2
Calibration in Survival
.......................... 271
15.3.3
Calibration-in-the-Large
......................... 271
15.3.4
Calibration Slope
.............................. 272
15.3.5
Estimation of Calibration-in-the-Large and
Calibration Slope
.............................. 272
15.3.6
Other Calibration Measures
...................... 273
15.3.7
Calibration Tests
............................... 274
15.3.8
Goodness-of-Fit Tests
........................... 274
15.3.9
Calibration of Survival Predictions
................. 276
15.3.10
Example: Calibration in Testicular Cancer
Prediction Model
............................... 276
15.3.11
Calibration and Discrimination
.................... 278
15.3.12
RCode
...................................... 278
15.4
Concluding Remarks
................................... 278
15.4.1
Bibliographic Notes
............................ 279
16
Clinical Usefulness
......................................... 281
16.1
Clinical Usefulness
.................................... 281
16.1.1
Intuitive Approach to the Cutoff
................... 282
16.1.2
Decision-Analytic Approach to the Cutoff
........... 282
16.1.3
Error Rate and Accuracy
......................... 283
16.1.4
Accuracy Measures for Clinical Usefulness
.......... 284
16.1.5
Decision Curves
............................... 284
16.1.6
Examples of NB in Decision Curves
............... 285
16.1.7
Example: Clinical Usefulness of Prediction Model for
Testicular Cancer
............................... 286
Contents xxiii
16.1.8
Decision Curves for Testicular Cancer Example
...... 287
16.1.9
Verification Bias and Clinical Usefulness
............ 288
16.1.10
R
Code
...................................... 289
16.2
Discrimination, Calibration, and Clinical Usefulness
.......... 289
16.2.1
Aim of the Prediction Model and Performance
Measures
..................................... 290
16.2.2
Summary Points
............................... 291
16.3
From Prediction Models to Decision Rules
................. 291
16.3.1
Performance of Decision Rules
................... 292
16.3.2
Treatment Benefit in Prognostic Subgroups
.......... 294
16.3.3
Evaluation of Classification Systems
............... 294
16.4
Concluding Remarks
................................... 295
16.4.1
Bibliographic notes
............................. 296
17
Validation of Prediction Models
............................... 299
17.1
Internal vs. External Validation, and Validity
................ 299
17.2
Internal Validation Techniques
........................... 300
17.2.1
Apparent Validation
............................ 300
17.2.2
Split-Sample Validation
......................... 301
17.2.3
Cross-Validation
............................... 302
17.2.4
Bootstrap Validation
............................ 303
17.3
External Validation Studies
.............................. 304
17.3.1
Temporal Validation
............................ 305
17.3.2
Example: Development and Validation of a
Model for Lynch Syndrome
...................... 306
17.3.3
Geographic Validation
.......................... 307
17.3.4
Fully Independent Validation
..................... 308
17.3.5
Reasons for Poor Validation
...................... 309
17.4
Concluding Remarks
................................... 310
18
Presentation Formats
....................................... 313
18.1
Prediction vs. Decision Rules
............................ 313
18.2
Clinical Prediction Models
.............................. 315
18.2.1
Regression Formula
............................ 315
18.2.2
Confidence Intervals for Predictions
................ 316
18.2.3
Nomograms
................................... 317
18.2.4
Score Chart
................................... 319
18.2.5
Tables with Predictions
.......................... 320
18.2.6
Specific Formats
............................... 321
1
8.3
Case Study: Clinical Prediction Model for Testicular
Cancer Model
........................................ 321
18.3.1
Regression Formula from Logistic Model
........... 321
18.3.2
Nomogram
................................... 324
Contents
18.3.3
Score
Chart................................... 324
18.3.4
Coding with Categorization
...................... 327
18.3.5
Summary Points
............................... 327
18.4
Clinical Decision Rules
................................. 328
18.4.1
Regression Tree
................................ 328
18.4.2
Score Chart Rule
............................... 328
18.4.3
Survival Groups
............................... 329
18.4.4
Meta-Model
................................... 329
18.5
Concluding Remarks
................................... 330
Part III Generalizability of Prediction Models
19
Patterns of External Validity
................................. 333
19.1
Determinants of External Validity
........................ 335
19.1.1
Case-Mix
..................................... 335
19.1.2
Differences in Case-Mix
......................... 336
19.1.3
Differences in Regression Coefficients
.............. 336
19.2
Impact on Calibration, Discrimination, and Clinical
Usefulness
........................................... 337
19.2.1
Simulation Set-Up
.............................. 338
19.2.2
Performance Measures
.......................... 339
19.3
Distribution of Predictors
............................... 340
19.3.1
More- or Less-Severe Case-Mix According to X
...... 340
19.3.2
Example: Interpretation of Testicular Cancer
Validation
.................................... 341
19.3.3
More or Less Heterogeneous Case-Mix
According to X
................................ 341
19.3.4
More- or Less-Severe Case-Mix According to
Z
...... 342
19.3.5
More or Less Heterogeneous Case-Mix
According to
Z
................................ 344
19.4
Distribution of Observed Outcomes
Y
...................... 344
19.5
Coefficients
β
......................................... 345
19.5.1
Coefficient of Linear Predictor
< 1................. 345
19.5.2
Coefficients Different
........................... 346
19.5.3
R
Code
...................................... 346
19.5.4
Influence of Different Coefficients
................ 347
19.5.5
Other Scenarios of Invalidity
..................... 348
19.5.6
Summary of Patterns of Invalidity
................. 348
19.6
Reference Values for Performance
........................ 349
19.6.1
Calculation of Reference Values
................... 349
19.6.2
R
Code
...................................... 350
19.6.3
Performance with Refitting
....................... 350
19.6.4
Examples: Testicular Cancer and TBI
.............. 351
Contents xxv
19.7
Estimation
of
Performance.............................. 352
19.7.
і
Uncertainty in Validation of Performance
........... 352
19.7.2
Estimating Standard Errors in Validation Studies
...... 354
19.7.3
Summary Points
............................... 354
19.8
Design of External Validation Studies
..................... 355
19.8.1
Power of External Validation Studies
............... 355
19.8.2
Required Sample Sizes for Validation Studies
........ 356
19.8.3
Summary Points
............................... 357
19.9
Concluding Remarks
................................... 358
20
Updating for a New Setting
.................................. 361
20.1
Updating the Intercept
.................................. 361
20.1.1
Simple Updating Methods
....................... 362
20.1.2
Bayesian Updating
............................. 362
20.2
Approaches to More-Extensive Updating
................... 363
20.2.1
A comparison of Eight Updating Methods
........... 364
20.3
Case Study: Validation and Updating in GUSTO-I
........... 366
20.3.1
Validity of TIMI-II Model for GUSTO-I
............ 366
20.3.2
Updating the TIMI-II Model for GUSTO-I
.......... 368
20.3.3
Performance of Updated Models
.................. 369
20.3.4
R
Code for Updating Methods
.................... 370
20.4
Shrinkage and Updating
................................ 371
20.4.1
Example: Shrinkage towards Re-calibrated Values in
GUSTO-I
..................................... 371
20.4.2
R
code for Shrinkage and Penalization in Updating
.... 372
20.5
Sample Size and Updating Strategy
....................... 373
20.5.1
Simulations of Sample Size, Shrinkage, and Updating
Strategy
...................................... 374
20.6
Validation and Updating of Tree Models
................... 376
20.6.1
Example: Tree Modelling in Testicular Cancer
....... 377
20.7
Validation and Updating of Survival Models
................ 378
20.7.1
Case Study: Validation of a Simple
Indexfor
Non-Hodgkin s Lymphoma
...................... 379
20.7.2
Updating the Prognostic Index
.................... 380
20.7.3
Re-calibration for Groups by Time Points
........... 380
20.7.4
Re-calibration with a Cox Regression Model
......... 381
20.7.5
Parametric Re-calibration
........................ 382
20.7.6
Summary Points
............................... 384
20.8
Continuous Updating
.................................. 384
20.8.1
A Continuous Updating Strategy
.................. 385
20.8.2
Example: Continuous Updating in GUSTO-I
......... 386
20.9
Concluding Remarks
................................... 388
xxvi
ContmtS
21
Updating
for Multiple Settings
................................ 391
21.1
Differences Between Settings
............................ 391
21.1.1
Testing for Calibration-in-the Large
................ 391
21.1.2
Illustration of Heterogeneity in GUSTO-I
........... 392
21.1.3
Updating for Better Calibration-in-the Large
......... 393
21.1.4
Empirical
Bayes
Estimates
....................... 394
21.1.5
Illustration of Updating in GUSTO-I
............... 394
21.1.6
Testing and Updating of Predictor Effects
........... 396
21.1.7
Heterogeneity of Predictor Effects in GUSTO-I
....... 396
21.1.8
R
Code for Random Effect Analyses
............... 397
21.2
Provider Profiling
..................................... 398
21.2.1
Indicators for Differences Between Centres
.......... 398
21.2.2
Ranking of Centres
............................. 399
21.2.3
Example: Provider Profiling in Stroke
.............. 401
21.2.4
Testing of Differences Between Centres
............. 401
21.2.5
Estimation of Differences Between Centres
.......... 402
21.2.6
Uncertainty in Differences
....................... 403
21.2.7
Ranking of Centres
............................. 404
21.2.8
Essential
R
Code for Provider Profiling
............. 405
21.2.9
Guidelines for Provider Profiling
.................. 406
21.3
Concluding Remarks
................................... 406
21.3.1
Bibliographic Notes
............................ 407
Part IV Applications
22
Prediction of a Binary Outcome: 30-Day Mortality
After Acute Myocardial Infarction
............................ 411
22.1
GUSTO-I Study
...................................... 411
22.1.1
Acute Myocardial Infarction
...................... 411
22.1.2
Treatment Results from GUSTO-I
................. 412
22.1.3
Prognostic Modelling in GUSTO-I
................. 412
22.2
General Considerations of Model Development
.............. 415
22.2.1
Research Question and Intended Application
......... 415
22.2.2
Outcome and Predictors
......................... 416
22.2.3
Study Design and Analysis
....................... 416
22.3
Seven Modelling Steps in GUSTO-I
....................... 417
22.3.1
Data Inspection
................................ 417
22.3.2
Coding of Predictors
............................ 418
22.3.3
Model Specification
............................ 418
22.3.4
Model Estimation
.............................. 418
22.3.5
Model Performance
............................. 419
22.3.6
Model Validation
............................... 419
22.3.7
Presentation
................................... 420
Contents xxvii
22.4
Validity
............................................. 421
22.4.1
Internal Validity: Overfitting
...................... 421
22.4.2
External Validity: Generalizability
................. 421
22.4.3
Summary Points
............................... 421
22.5
Translation into Clinical Practice
......................... 422
22.5.1
Score Chart for Choosing Thrombolytic Therapy
..... 422
22.5.2
Predictions for Choosing Thrombolytic Therapy
...... 423
22.5.3
Covariate Adjustment in GUSTO-I
................ 424
22.6
Concluding Remarks
................................... 425
23
Case Study on Survival Analysis: Prediction
of Secondary Cardiovascular Events
.......................... 427
23.1
Prognosis in the SMART Study
.......................... 427
23.1.1
Patients in SMART
............................. 428
23.2
General Considerations in SMART
....................... 429
23.2.1
Research Question and Intended Application
......... 429
23.2.2
Outcome and Predictors
......................... 429
23.2.3
Study Design and Analysis
....................... 432
23.3
Data Inspection Steps in the SMART Cohort
................ 432
23.4
Coding of Predictors
................................... 435
23.4.1
Extreme Values
................................ 435
23.4.2
Transforming Continuous Predictors
............... 436
23.4.2
Combining Predictors with Similar Effects
.......... 437
23.5
Model Specification
................................... 438
23.5.1
Selection
..................................... 440
23.6
Model Estimation, Performance, Validation, and Presentation
.. 440
23.6.1
Model Estimation
.............................. 440
23.6.2
Model Performance
............................. 442
23.6.3
Model Validation: Stability
....................... 442
23.6.4
Model Validation: Optimism
...................... 444
23.6.5
Model Presentation
............................. 444
23.7
Concluding Remarks
................................... 444
24
Lessons from Case Studies
................................... 447
24.1
Sample Size
.......................................... 447
24.1.1
Example: Sample Size and Number of Predictors
..... 447
24.1.2
Number of Predictors
........................... 448
24.1.3
Potential Solutions
............................. 449
24.2
Validation
........................................... 450
24.2.1
Examples of Internal and External Validation
........ 450
24.3
Subject Matter Knowledge
.............................. 451
24.4
Data Sets
............................................ 452
24.4.1
GUSTO-I Prediction Models
..................... 453
24.4.2
Modern Learning Methods in GUSTO-I
............ 453
xxviii Contents
24.4.3
Modelling Strategies in Small Data Sets
from GUSTO-I
................................ 453
24.4.4
SMART Case Study
............................ 453
24.4.5
Testicular Cancer Case Study
..................... 455
24.4.6
Abdominal Aortic Aneurysm Case Study
............ 455
24.4.7
Traumatic Brain Injury Data Set
................... 459
24.5
Concluding Remarks
................................... 459
References
.................................................... 463
Index
........................................................ 487
|
| any_adam_object | 1 |
| author | Steyerberg, Ewout W. 1967- |
| author_GND | (DE-588)1057801720 |
| author_facet | Steyerberg, Ewout W. 1967- |
| author_role | aut |
| author_sort | Steyerberg, Ewout W. 1967- |
| author_variant | e w s ew ews |
| building | Verbundindex |
| bvnumber | BV035368790 |
| callnumber-first | R - Medicine |
| callnumber-label | RA409 |
| callnumber-raw | RA409 |
| callnumber-search | RA409 |
| callnumber-sort | RA 3409 |
| callnumber-subject | RA - Public Medicine |
| classification_rvk | WC 7700 XF 3500 |
| classification_tum | MED 230f MAT 628f |
| ctrlnum | (OCoLC)295033353 (DE-599)DNB988550016 |
| dewey-full | 610.727 |
| dewey-hundreds | 600 - Technology (Applied sciences) |
| dewey-ones | 610 - Medicine and health |
| dewey-raw | 610.727 |
| dewey-search | 610.727 |
| dewey-sort | 3610.727 |
| dewey-tens | 610 - Medicine and health |
| discipline | Biologie Mathematik Medizin |
| format | Book |
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| id | DE-604.BV035368790 |
| illustrated | Not Illustrated |
| indexdate | 2024-07-09T21:32:17Z |
| institution | BVB |
| isbn | 038777243X 9780387772431 |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-017172709 |
| oclc_num | 295033353 |
| open_access_boolean | |
| owner | DE-355 DE-BY-UBR DE-19 DE-BY-UBM DE-91G DE-BY-TUM DE-188 |
| owner_facet | DE-355 DE-BY-UBR DE-19 DE-BY-UBM DE-91G DE-BY-TUM DE-188 |
| physical | XXVIII, 497 S. |
| publishDate | 2009 |
| publishDateSearch | 2009 |
| publishDateSort | 2009 |
| publisher | Springer |
| record_format | marc |
| series2 | Statistics for biology and health |
| spelling | Steyerberg, Ewout W. 1967- Verfasser (DE-588)1057801720 aut Clinical prediction models a practical approach to development, validation, and updating Ewout W. Steyerberg New York, NY Springer 2009 XXVIII, 497 S. txt rdacontent n rdamedia nc rdacarrier Statistics for biology and health prediction cabt statistics cabt data analysis cabt data processing cabt mathematical models cabt simulation models cabt epidemiology cabt Analyse de régression Médecine - Recherche - Statistiques ram Médecine fondée sur la preuve - statistiques et données numériques Statistiques médicales ram Études cliniques - Statistiques ram Medizin Clinical trials Statistical methods Evidence-based medicine Statistical methods Medical statistics Medicine Research Statistical methods Models, Statistical Prognosis Regression Analysis Regression analysis Medizinische Statistik (DE-588)4127563-9 gnd rswk-swf Medizinische Statistik (DE-588)4127563-9 s DE-604 Erscheint auch als Online-Ausgabe 978-0-387-77244-8 Digitalisierung UB Regensburg application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=017172709&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis |
| spellingShingle | Steyerberg, Ewout W. 1967- Clinical prediction models a practical approach to development, validation, and updating prediction cabt statistics cabt data analysis cabt data processing cabt mathematical models cabt simulation models cabt epidemiology cabt Analyse de régression Médecine - Recherche - Statistiques ram Médecine fondée sur la preuve - statistiques et données numériques Statistiques médicales ram Études cliniques - Statistiques ram Medizin Clinical trials Statistical methods Evidence-based medicine Statistical methods Medical statistics Medicine Research Statistical methods Models, Statistical Prognosis Regression Analysis Regression analysis Medizinische Statistik (DE-588)4127563-9 gnd |
| subject_GND | (DE-588)4127563-9 |
| title | Clinical prediction models a practical approach to development, validation, and updating |
| title_auth | Clinical prediction models a practical approach to development, validation, and updating |
| title_exact_search | Clinical prediction models a practical approach to development, validation, and updating |
| title_full | Clinical prediction models a practical approach to development, validation, and updating Ewout W. Steyerberg |
| title_fullStr | Clinical prediction models a practical approach to development, validation, and updating Ewout W. Steyerberg |
| title_full_unstemmed | Clinical prediction models a practical approach to development, validation, and updating Ewout W. Steyerberg |
| title_short | Clinical prediction models |
| title_sort | clinical prediction models a practical approach to development validation and updating |
| title_sub | a practical approach to development, validation, and updating |
| topic | prediction cabt statistics cabt data analysis cabt data processing cabt mathematical models cabt simulation models cabt epidemiology cabt Analyse de régression Médecine - Recherche - Statistiques ram Médecine fondée sur la preuve - statistiques et données numériques Statistiques médicales ram Études cliniques - Statistiques ram Medizin Clinical trials Statistical methods Evidence-based medicine Statistical methods Medical statistics Medicine Research Statistical methods Models, Statistical Prognosis Regression Analysis Regression analysis Medizinische Statistik (DE-588)4127563-9 gnd |
| topic_facet | prediction statistics data analysis data processing mathematical models simulation models epidemiology Analyse de régression Médecine - Recherche - Statistiques Médecine fondée sur la preuve - statistiques et données numériques Statistiques médicales Études cliniques - Statistiques Medizin Clinical trials Statistical methods Evidence-based medicine Statistical methods Medical statistics Medicine Research Statistical methods Models, Statistical Prognosis Regression Analysis Regression analysis Medizinische Statistik |
| url | http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=017172709&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |
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