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This book aims to provide insight and practical illustrations on how modern statistical concepts and regression methods can be applied in medical prediction problems, including diagnostic and prognostic outcomes. Many advances have been made in statistical approaches towards outcome prediction, but these innovations are insufficiently applied in medical research. Old-fashioned, data hungry methods are often used in data sets of limited size, validation of predictions is not done or only in a simplistic way, and updating of already available models is not considered. A sensible strategy is…mehr

Produktbeschreibung
This book aims to provide insight and practical illustrations on how modern statistical concepts and regression methods can be applied in medical prediction problems, including diagnostic and prognostic outcomes. Many advances have been made in statistical approaches towards outcome prediction, but these innovations are insufficiently applied in medical research. Old-fashioned, data hungry methods are often used in data sets of limited size, validation of predictions is not done or only in a simplistic way, and updating of already available models is not considered. A sensible strategy is needed for model development, validation, and updating, such that prediction models can better support medical practice. Clinical Prediction Models presents a practical checklist with seven steps that need to be considered for development of a valid prediction model. These include preliminary considerations such as dealing with missing values; coding of predictors; selection of main effects and interactions for a multivariable model; estimation of model parameters with shrinkage methods and incorporation of external data; evaluation of performance and clinical usefulness; internal validation; and presentation format. The steps are illustrated with many small case studies and R computer code, with data sets made available in the public domain [http://www.clinicalpredictionmodels.org/]. The book further focuses on generalizability of prediction models, including patterns of invalidity that may be encountered in new settings, approaches to modifying and extending a model, and comparisons of centers after case-mix adjustment by a prediction model. The text is primarily intended for epidemiologists and applied biostatisticians. It can be used as a textbook for a graduate course on predictive modeling in diagnosis and prognosis. It is beneficial if readers are familiar with common statistical models in medicine: linear regression, logistic regression, and Cox regression. The book is practical in nature. But it also provides a philosophical perspective on data analysis in medicine that goes beyond predictive modeling. In this era of evidence-based medicine, randomized clinical trials are the basis for assessment of treatment efficacy. Prediction models are key to individualizing diagnostic and treatment decision-making.

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  • Produktdetails
  • Verlag: Springer-Verlag GmbH
  • Erscheinungstermin: 16.12.2008
  • Englisch
  • ISBN-13: 9780387772448
  • Artikelnr.: 37287661
Autorenporträt
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.
Inhaltsangabe
Introduction.- Applications of prediction models.- Study design for prediction models.- Statistical models for prediction.- Overfitting and optimism in prediction models.- Choosing between alternative statistical models.- Dealing with missing values.- Case study on dealing with missing values.- Coding of categorical and continuous predictors.- Restrictions on candidate predictors.- Selection of main effects.- Assumptions in regression models: Additivity and linearity.- Modern estimation methods.- Estimation with external methods.- Evaluation of performance.- Clinical usefulness.- Validation of prediction models.- Presentation formats.- Patterns of external validity.- Updating for a new setting.- Updating for a multiple settings.- Prediction of a binary outcome: 30-day mortality after acute myocardial infarction.- Case study on survival analysis: Prediction of secondary cardiovascular events.- Lessons from case studies.

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 o
Rezensionen
From the reviews: "This book covers an important topic, because these prediction models are essential for individualizing diagnostic and treatment decision making. The topic is of increased importance as evidence-based medicine is increasingly implemented and as scientific and technological advances reveal new potential predictors of outcome. This book presents an approach for developing, validating, and updating prediction models.... [I]t provides ways to optimally utilize regression techniques to predict an outcome.... This book is written in a clear and accessible style.... [A]valuable resource for anyone interested in developing or applying a prediction model." (Todd A. Alonzo, American Journal of Epidemiology , 2009; Vol. 170, No. 4) "Overall I think this is a well-written book, which will have a wide appeal. The idea of defining a strategy to deal with clinical prediction problems might be somewhat controversial, but considering the variable quality of statistical analyses that appear in the medical literature, I believe such an approach is desirable. The book appears to have struck a good balance between practical examples and descriptions of statistical techniques.... It is refreshing to see a practical book applying many modern regression techniques to real problems." (David Ohlssen, Journal of Biopharmaceutical Statistics , Issue 6, 2009) "Dr Steyerberg ... aims to provide an insight and also a practical illustration on how modern statistical concepts and regression methods can be applied in medical prediction outcomes. The book...will be of interest to those who work in medical cybernetics and indeed all cybernetics and systems researchers who are studying such medical problems and wish to apply statistical approaches and methodologies. It is worth examining the detailed contents list ... and individual chapters may be of particular value to potential readers." (C. J. H. Mann, Kybernetes , Vol. 38 (6), 2009) "The book ... will be of interest to those who work in medical cybernetics and indeed all cybernetics and systems researchers who are studying such medical problems and wish to apply statistical approaches and methodologies." (C. J. H. Mann, Kybernetes , Vol. 38, No. 6, 2009) "...and excellent practical guide for developing, assessing and updating clinical models both for disease prognosis and diagnosis. The book's clinical focus in this era of evidence-based medicine is refreshing and serves as a much-needed addition to statistical modeling of clinical data. The book assumes a basic familiarity with modeling using generalized linear models, focusing instead on the real challenges facing applied biostatisticians and epidemiologists wanting to create useful models: dealing with a plethora of model choices, small sample sizes, many candidate predictors and missing data. This is an example-based book illuminating the vagaries of clinical data and offering sound practical advice on data exploration, model selection and data presentation. ...The author uses simple simulations using a few reproducible R commands to motivate the use of imputation methods and shrinkage. These simple but illuminating illustrations are one of the highlights of the book and serve as excellent pedagogical tools for motivating good statistical thinking. ..." (International Statistical Review 2009, 77, 2) "This is an excellent text that should be read by anyone performing prediction modeling. ... the text has three audiences epidemiologists and applied biostatisticians who want to develop or apply a prediction model health care professionals who want to judge a study that presents a prediction model and theoretical researchers ... . I found the book very useful and I believe clinicians and policy makers will be similarly well served. ... All are excellent summaries for readers and provide links to resources for further investigation." (Chris Andrews, Technometrics, Vol. 53 (1), February, 2011)…mehr