Applied Bayesian statistical studies in biology and medicine / edited by M. Di Bacco, G. D'Amore, F. Scalfari |
Pubbl/distr/stampa | Boston ; Dordrecht ; London : Kluwer Academic Publishers, c2004 |
Descrizione fisica | xvii, 258 p. : ill. ; 25 cm |
Disciplina | 610.727 |
Altri autori (Persone) |
Di Bacco, Mario
D'Amore, Giuseppe Scalfari, Francesco |
Soggetto topico |
Medicine - Research - Statistical methods
Bayesian statistical decision theory |
ISBN | 1402075480 |
Classificazione |
AMS 62P10
LC R853.S7A675 |
Formato | Materiale a stampa ![]() |
Livello bibliografico | Monografia |
Lingua di pubblicazione | eng |
Record Nr. | UNISALENTO-991000758379707536 |
Boston ; Dordrecht ; London : Kluwer Academic Publishers, c2004 | ||
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Lo trovi qui: Univ. del Salento | ||
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Applied medical statistics / / Jingmei Jiang |
Autore | Jiang Jingmei <1958-> |
Pubbl/distr/stampa | Hoboken, NJ : , : John Wiley & Sons, Inc., , [2022] |
Descrizione fisica | 1 online resource (585 pages) : illustrations |
Disciplina | 570.15195 |
Soggetto topico |
Biometry
Medical statistics Medicine - Research - Statistical methods |
Soggetto genere / forma | Electronic books. |
ISBN |
1-119-71682-9
1-119-71677-2 9781119716822 1119716829 9781119716778 1119716772 9781119716709 |
Formato | Materiale a stampa ![]() |
Livello bibliografico | Monografia |
Lingua di pubblicazione | eng |
Nota di contenuto | What is biostatistics -- Descriptive statistics -- Fundamentals of probability -- Discrete random variable -- Continuous random variable -- Sampling distribution and parameter estimation -- Hypothesis testing for one parameter -- Hypothesis testing for two population parameters -- One-way analysis of variance -- Analysis of variance in different experimental -- X2 test -- Nonparametric tests based on rank -- Simple linear regression -- Simple linear correlation -- Multiple linear regression -- Logistic regression -- Survival analysis -- Evaluation of diagnostic tests -- Observational study design -- Experimental study design. |
Record Nr. | UNINA-9910566698903321 |
Jiang Jingmei <1958->
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Hoboken, NJ : , : John Wiley & Sons, Inc., , [2022] | ||
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Lo trovi qui: Univ. Federico II | ||
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Applied medical statistics / / Jingmei Jiang |
Autore | Jiang Jingmei <1958-> |
Pubbl/distr/stampa | Hoboken, NJ : , : Wiley Blackwell, , ℗2022 |
Descrizione fisica | 1 online resource (585 pages) : illustrations |
Disciplina | 570.15195 |
Soggetto topico |
Biometry
Medical statistics Medicine - Research - Statistical methods |
ISBN |
1-119-71682-9
1-119-71677-2 |
Formato | Materiale a stampa ![]() |
Livello bibliografico | Monografia |
Lingua di pubblicazione | eng |
Nota di contenuto | What is biostatistics -- Descriptive statistics -- Fundamentals of probability -- Discrete random variable -- Continuous random variable -- Sampling distribution and parameter estimation -- Hypothesis testing for one parameter -- Hypothesis testing for two population parameters -- One-way analysis of variance -- Analysis of variance in different experimental -- X2 test -- Nonparametric tests based on rank -- Simple linear regression -- Simple linear correlation -- Multiple linear regression -- Logistic regression -- Survival analysis -- Evaluation of diagnostic tests -- Observational study design -- Experimental study design. |
Record Nr. | UNINA-9910830255103321 |
Jiang Jingmei <1958->
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Hoboken, NJ : , : Wiley Blackwell, , ℗2022 | ||
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Lo trovi qui: Univ. Federico II | ||
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Applied survival analysis : regression modeling of time-to-event data / / David W. Hosmer, Stanley Lemeshow, Susanne May / / David W. Hosmer, Stanley Lemeshow, Susanne May |
Autore | Hosmer David W. |
Edizione | [2nd ed.] |
Pubbl/distr/stampa | Hoboken, New Jersey : , : Wiley-Interscience, , 2008 |
Descrizione fisica | 1 online resource (418 p.) |
Disciplina |
610.72
610.727 |
Collana | Wiley Series in Probability and Statistics |
Soggetto topico |
Medicine - Research - Statistical methods
Medical sciences - Statistical methods - Computer programs Regression analysis - Data processing Prognosis - Statistical methods Logistic distribution |
ISBN |
1-283-28250-X
9786613282507 0-470-25801-2 0-470-25800-4 |
Formato | Materiale a stampa ![]() |
Livello bibliografico | Monografia |
Lingua di pubblicazione | eng |
Nota di contenuto |
Applied Survival Analysis: Regression Modeling of Time-to-Event Data; Contents; Preface; 1 Introduction to Regression Modeling of Survival Data; 1.1 Introduction; 1.2 Typical Censoring Mechanisms; 1.3 Example Data Sets; Exercises; 2 Descriptive Methods for Survival Data; 2.1 Introduction; 2.2 Estimating the Survival Function; 2.3 Using the Estimated Survival Function; 2.4 Comparison of Survival Functions; 2.5 Other Functions of Survival Time and Their Estimators; Exercises; 3. Regression Models for Survival Data; 3.1 Introduction; 3.2 Semi-Parametric Regression Models
3.3 Fitting the Proportional Hazards Regression Model3.4 Fitting the Proportional Hazards Model with Tied Survival Times; 3.5 Estimating the Survival Function of the Proportional Hazards Regression Model; Exercises; 4. Interpretation of a Fitted Proportional Hazards Regression Model; 4.1 Introduction; 4.2 Nominal Scale Covariate; 4.3 Continuous Scale Covariate; 4.4 Multiple-Covariate Models; 4.5 Interpreting and Using the Estimated Covariate-Adjusted Survival Function; Exercises; 5. Model Development; 5.1 Introduction; 5.2 Purposeful Selection of Covariates 5.2.1 Methods to examine the scale of continuous covariates in the log hazard5.2.2 An example of purposeful selection of covariates; 5.3 Stepwise, Best-Subsets and Multivariable Fractional PolynomialMethods of Selecting Covariates; 5.3.1 Stepwise selection of covariates; 5.3.2 Best subsets selection of covariates; 5.3.3 Selecting covariates and checking their scale using multivariable fractional polynomials; 5.4 Numerical Problems; Exercises; 6. Assessment of Model Adequacy; 6.1 Introduction; 6.2 Residuals; 6.3 Assessing the Proportional Hazards Assumption 6.4 Identification of Influential and Poorly Fit Subjects6.5 Assessing Overall Goodness-of-Fit; 6.6 Interpreting and Presenting Results From the Final Model; Exercises; 7. Extensions of the Proportional Hazards Model; 7.1 Introduction; 7.2 The Stratified Proportional Hazards Model; 7.3 Time-Varying Covariates; 7.4 Truncated, Left Censored and Interval Censored Data; Exercises; 8. Parametric Regression Models; 8.1 Introduction; 8.2 The Exponential Regression Model; 8.3 The Weibull Regression Model; 8.4 The Log-Logistic Regression Model; 8.5 Other Parametric Regression Models; Exercises 9. Other Models and Topics9.1 Introduction; 9.2 Recurrent Event Models; 9.3 Frailty Models; 9.4 Nested Case-Control Studies; 9.5 Additive Models; 9.6 Competing Risk Models; 9.7 Sample Size and Power; 9.8 Missing Data; Exercises; Appendix 1 The Delta Method; Appendix 2 An Introduction to the Counting Process Approach to Survival Analysis; Appendix 3 Percentiles for Computation of the Hall and Wellner Confidence Band; References; Index |
Record Nr. | UNINA-9910130964503321 |
Hosmer David W.
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Hoboken, New Jersey : , : Wiley-Interscience, , 2008 | ||
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Lo trovi qui: Univ. Federico II | ||
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Bayesian approaches to clinical trials and health care evaluation [[electronic resource] /] / David J. Spiegelhalter, Keith R. Abrams, Jonathan P. Myles |
Autore | Spiegelhalter D. J |
Pubbl/distr/stampa | Chichester ; ; Hoboken, NJ, : Wiley, c2004 |
Descrizione fisica | 1 online resource (408 p.) |
Disciplina |
519.5/42/02461
610.72 |
Altri autori (Persone) |
AbramsK. R (Keith R.)
MylesJonathan P |
Collana | Statistics in practice |
Soggetto topico |
Bayesian statistical decision theory
Medicine - Research - Statistical methods Clinical trials - Statistical methods |
Soggetto genere / forma | Electronic books. |
ISBN |
1-280-26930-8
9786610269303 0-470-09259-9 0-470-09260-2 |
Formato | Materiale a stampa ![]() |
Livello bibliografico | Monografia |
Lingua di pubblicazione | eng |
Nota di contenuto |
Bayesian Approaches to Clinical Trials and Health-Care Evaluation; Contents; Preface; List of examples; 1 Introduction; 1.1 What are Bayesian methods?; 1.2 What do we mean by 'health-care evaluation'?; 1.3 A Bayesian approach to evaluation; 1.4 The aim of this book and the intended audience; 1.5 Structure of the book; 2 Basic Concepts from Traditional Statistical Analysis; 2.1 Probability; 2.1.1 What is probability?; 2.1.2 Odds and log-odds; 2.1.3 Bayes theorem for simple events; 2.2 Random variables, parameters and likelihood; 2.2.1 Random variables and their distributions
2.2.2 Expectation, variance, covariance and correlation 2.2.3 Parametric distributions and conditional independence; 2.2.4 Likelihoods; 2.3 The normal distribution; 2.4 Normal likelihoods; 2.4.1 Normal approximations for binary data; 2.4.2 Normal likelihoods for survival data; 2.4.3 Normal likelihoods for count responses; 2.4.4 Normal likelihoods for continuous responses; 2.5 Classical inference; 2.6 A catalogue of useful distributions*; 2.6.1 Binomial and Bernoulli; 2.6.2 Poisson; 2.6.3 Beta; 2.6.4 Uniform; 2.6.5 Gamma; 2.6.6 Root-inverse-gamma; 2.6.7 Half-normal; 2.6.8 Log-normal 2.6.9 Student's 2.6.10 Bivariate normal; 2.7 Key points; Exercises; 3 An Overview of the Bayesian Approach; 3.1 Subjectivity and context; 3.2 Bayes theorem for two hypotheses; 3.3 Comparing simple hypotheses: likelihood ratios and Bayes factors; 3.4 Exchangeability and parametric modelling*; 3.5 Bayes theorem for general quantities; 3.6 Bayesian analysis with binary data; 3.6.1 Binary data with a discrete prior distribution; 3.6.2 Conjugate analysis for binary data; 3.7 Bayesian analysis with normal distributions; 3.8 Point estimation, interval estimation and interval hypotheses 3.9 The prior distribution 3.10 How to use Bayes theorem to interpret trial results; 3.11 The 'credibility' of significant trial results*; 3.12 Sequential use of Bayes theorem*; 3.13 Predictions; 3.13.1 Predictions in the Bayesian framework; 3.13.2 Predictions for binary data*; 3.13.3 Predictions for normal data; 3.14 Decision-making; 3.15 Design; 3.16 Use of historical data; 3.17 Multiplicity, exchangeability and hierarchical models; 3.18 Dealing with nuisance parameters*; 3.18.1 Alternative methods for eliminating nuisance parameters*; 3.18.2 Profile likelihood in a hierarchical model* 3.19 Computational issues 3.19.1 Monte Carlo methods; 3.19.2 Markov chain Monte Carlo methods; 3.19.3 WinBUGS; 3.20 Schools of Bayesians; 3.21 A Bayesian checklist; 3.22 Further reading; 3.23 Key points; Exercises; 4 Comparison of Alternative Approaches to Inference; 4.1 A structure for alternative approaches; 4.2 Conventional statistical methods used in health-care evaluation; 4.3 The likelihood principle, sequential analysis and types of error; 4.3.1 The likelihood principle; 4.3.2 Sequential analysis; 4.3.3 Type I and Type II error; 4.4 P-values and Bayes factors* 4.4.1 Criticism of P-values |
Record Nr. | UNINA-9910143509603321 |
Spiegelhalter D. J
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Chichester ; ; Hoboken, NJ, : Wiley, c2004 | ||
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Lo trovi qui: Univ. Federico II | ||
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Bayesian approaches to clinical trials and health care evaluation [[electronic resource] /] / David J. Spiegelhalter, Keith R. Abrams, Jonathan P. Myles |
Autore | Spiegelhalter D. J |
Pubbl/distr/stampa | Chichester ; ; Hoboken, NJ, : Wiley, c2004 |
Descrizione fisica | 1 online resource (408 p.) |
Disciplina |
519.5/42/02461
610.72 |
Altri autori (Persone) |
AbramsK. R (Keith R.)
MylesJonathan P |
Collana | Statistics in practice |
Soggetto topico |
Bayesian statistical decision theory
Medicine - Research - Statistical methods Clinical trials - Statistical methods |
ISBN |
1-280-26930-8
9786610269303 0-470-09259-9 0-470-09260-2 |
Formato | Materiale a stampa ![]() |
Livello bibliografico | Monografia |
Lingua di pubblicazione | eng |
Nota di contenuto |
Bayesian Approaches to Clinical Trials and Health-Care Evaluation; Contents; Preface; List of examples; 1 Introduction; 1.1 What are Bayesian methods?; 1.2 What do we mean by 'health-care evaluation'?; 1.3 A Bayesian approach to evaluation; 1.4 The aim of this book and the intended audience; 1.5 Structure of the book; 2 Basic Concepts from Traditional Statistical Analysis; 2.1 Probability; 2.1.1 What is probability?; 2.1.2 Odds and log-odds; 2.1.3 Bayes theorem for simple events; 2.2 Random variables, parameters and likelihood; 2.2.1 Random variables and their distributions
2.2.2 Expectation, variance, covariance and correlation 2.2.3 Parametric distributions and conditional independence; 2.2.4 Likelihoods; 2.3 The normal distribution; 2.4 Normal likelihoods; 2.4.1 Normal approximations for binary data; 2.4.2 Normal likelihoods for survival data; 2.4.3 Normal likelihoods for count responses; 2.4.4 Normal likelihoods for continuous responses; 2.5 Classical inference; 2.6 A catalogue of useful distributions*; 2.6.1 Binomial and Bernoulli; 2.6.2 Poisson; 2.6.3 Beta; 2.6.4 Uniform; 2.6.5 Gamma; 2.6.6 Root-inverse-gamma; 2.6.7 Half-normal; 2.6.8 Log-normal 2.6.9 Student's 2.6.10 Bivariate normal; 2.7 Key points; Exercises; 3 An Overview of the Bayesian Approach; 3.1 Subjectivity and context; 3.2 Bayes theorem for two hypotheses; 3.3 Comparing simple hypotheses: likelihood ratios and Bayes factors; 3.4 Exchangeability and parametric modelling*; 3.5 Bayes theorem for general quantities; 3.6 Bayesian analysis with binary data; 3.6.1 Binary data with a discrete prior distribution; 3.6.2 Conjugate analysis for binary data; 3.7 Bayesian analysis with normal distributions; 3.8 Point estimation, interval estimation and interval hypotheses 3.9 The prior distribution 3.10 How to use Bayes theorem to interpret trial results; 3.11 The 'credibility' of significant trial results*; 3.12 Sequential use of Bayes theorem*; 3.13 Predictions; 3.13.1 Predictions in the Bayesian framework; 3.13.2 Predictions for binary data*; 3.13.3 Predictions for normal data; 3.14 Decision-making; 3.15 Design; 3.16 Use of historical data; 3.17 Multiplicity, exchangeability and hierarchical models; 3.18 Dealing with nuisance parameters*; 3.18.1 Alternative methods for eliminating nuisance parameters*; 3.18.2 Profile likelihood in a hierarchical model* 3.19 Computational issues 3.19.1 Monte Carlo methods; 3.19.2 Markov chain Monte Carlo methods; 3.19.3 WinBUGS; 3.20 Schools of Bayesians; 3.21 A Bayesian checklist; 3.22 Further reading; 3.23 Key points; Exercises; 4 Comparison of Alternative Approaches to Inference; 4.1 A structure for alternative approaches; 4.2 Conventional statistical methods used in health-care evaluation; 4.3 The likelihood principle, sequential analysis and types of error; 4.3.1 The likelihood principle; 4.3.2 Sequential analysis; 4.3.3 Type I and Type II error; 4.4 P-values and Bayes factors* 4.4.1 Criticism of P-values |
Record Nr. | UNINA-9910830922703321 |
Spiegelhalter D. J
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||
Chichester ; ; Hoboken, NJ, : Wiley, c2004 | ||
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Lo trovi qui: Univ. Federico II | ||
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Bayesian approaches to clinical trials and health care evaluation / / David J. Spiegelhalter, Keith R. Abrams, Jonathan P. Myles |
Autore | Spiegelhalter D. J |
Pubbl/distr/stampa | Chichester ; ; Hoboken, NJ, : Wiley, c2004 |
Descrizione fisica | 1 online resource (408 p.) |
Disciplina | 519.5/42/02461 |
Altri autori (Persone) |
AbramsK. R (Keith R.)
MylesJonathan P |
Collana | Statistics in practice |
Soggetto topico |
Bayesian statistical decision theory
Medicine - Research - Statistical methods Clinical trials - Statistical methods |
ISBN |
1-280-26930-8
9786610269303 0-470-09259-9 0-470-09260-2 |
Formato | Materiale a stampa ![]() |
Livello bibliografico | Monografia |
Lingua di pubblicazione | eng |
Nota di contenuto |
Bayesian Approaches to Clinical Trials and Health-Care Evaluation; Contents; Preface; List of examples; 1 Introduction; 1.1 What are Bayesian methods?; 1.2 What do we mean by 'health-care evaluation'?; 1.3 A Bayesian approach to evaluation; 1.4 The aim of this book and the intended audience; 1.5 Structure of the book; 2 Basic Concepts from Traditional Statistical Analysis; 2.1 Probability; 2.1.1 What is probability?; 2.1.2 Odds and log-odds; 2.1.3 Bayes theorem for simple events; 2.2 Random variables, parameters and likelihood; 2.2.1 Random variables and their distributions
2.2.2 Expectation, variance, covariance and correlation 2.2.3 Parametric distributions and conditional independence; 2.2.4 Likelihoods; 2.3 The normal distribution; 2.4 Normal likelihoods; 2.4.1 Normal approximations for binary data; 2.4.2 Normal likelihoods for survival data; 2.4.3 Normal likelihoods for count responses; 2.4.4 Normal likelihoods for continuous responses; 2.5 Classical inference; 2.6 A catalogue of useful distributions*; 2.6.1 Binomial and Bernoulli; 2.6.2 Poisson; 2.6.3 Beta; 2.6.4 Uniform; 2.6.5 Gamma; 2.6.6 Root-inverse-gamma; 2.6.7 Half-normal; 2.6.8 Log-normal 2.6.9 Student's 2.6.10 Bivariate normal; 2.7 Key points; Exercises; 3 An Overview of the Bayesian Approach; 3.1 Subjectivity and context; 3.2 Bayes theorem for two hypotheses; 3.3 Comparing simple hypotheses: likelihood ratios and Bayes factors; 3.4 Exchangeability and parametric modelling*; 3.5 Bayes theorem for general quantities; 3.6 Bayesian analysis with binary data; 3.6.1 Binary data with a discrete prior distribution; 3.6.2 Conjugate analysis for binary data; 3.7 Bayesian analysis with normal distributions; 3.8 Point estimation, interval estimation and interval hypotheses 3.9 The prior distribution 3.10 How to use Bayes theorem to interpret trial results; 3.11 The 'credibility' of significant trial results*; 3.12 Sequential use of Bayes theorem*; 3.13 Predictions; 3.13.1 Predictions in the Bayesian framework; 3.13.2 Predictions for binary data*; 3.13.3 Predictions for normal data; 3.14 Decision-making; 3.15 Design; 3.16 Use of historical data; 3.17 Multiplicity, exchangeability and hierarchical models; 3.18 Dealing with nuisance parameters*; 3.18.1 Alternative methods for eliminating nuisance parameters*; 3.18.2 Profile likelihood in a hierarchical model* 3.19 Computational issues 3.19.1 Monte Carlo methods; 3.19.2 Markov chain Monte Carlo methods; 3.19.3 WinBUGS; 3.20 Schools of Bayesians; 3.21 A Bayesian checklist; 3.22 Further reading; 3.23 Key points; Exercises; 4 Comparison of Alternative Approaches to Inference; 4.1 A structure for alternative approaches; 4.2 Conventional statistical methods used in health-care evaluation; 4.3 The likelihood principle, sequential analysis and types of error; 4.3.1 The likelihood principle; 4.3.2 Sequential analysis; 4.3.3 Type I and Type II error; 4.4 P-values and Bayes factors* 4.4.1 Criticism of P-values |
Record Nr. | UNINA-9910877647303321 |
Spiegelhalter D. J
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Chichester ; ; Hoboken, NJ, : Wiley, c2004 | ||
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Lo trovi qui: Univ. Federico II | ||
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Biostatistical methods [[electronic resource] ] : the assessment of relative risks / / John M. Lachin |
Autore | Lachin John M. <1942-> |
Edizione | [2nd ed.] |
Pubbl/distr/stampa | Hoboken, N.J., : Wiley, c2011 |
Descrizione fisica | 1 online resource (xxiii, 622 pages) : illustrations |
Disciplina | 610.72 |
Collana | Wiley series in probability and statistics |
Soggetto topico |
Medical statistics
Health risk assessment - Statistical methods Medicine - Research - Statistical methods |
ISBN |
1-118-62584-6
1-283-37263-0 9786613372635 0-470-90741-X 0-470-90740-1 |
Formato | Materiale a stampa ![]() |
Livello bibliografico | Monografia |
Lingua di pubblicazione | eng |
Nota di contenuto | Biostatistics and Biomedical Science -- Relative Risk Estimates and Tests for Independent Groups -- Sample Size, Power, and Efficiency -- Stratified-Adjusted Analysis for Independent Groups -- Case-Control and Matched Studies -- Applications of Maximum Likelihood and Efficient Scores -- Logistic Regression Models -- Analysis of Count Data -- Analysis of Event-Time Data -- Appendix: Statistical Theory |
Record Nr. | UNINA-9910133222803321 |
Lachin John M. <1942->
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Hoboken, N.J., : Wiley, c2011 | ||
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Lo trovi qui: Univ. Federico II | ||
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Biostatistical methods : the assessment of relative risks / / John M. Lachin |
Autore | Lachin John M. <1942-> |
Edizione | [2nd ed.] |
Pubbl/distr/stampa | Hoboken, N.J., : Wiley, c2011 |
Descrizione fisica | 1 online resource (xxiii, 622 pages) : illustrations |
Disciplina | 610.72 |
Collana | Wiley series in probability and statistics |
Soggetto topico |
Medical statistics
Health risk assessment - Statistical methods Medicine - Research - Statistical methods |
ISBN |
1-118-62584-6
1-283-37263-0 9786613372635 0-470-90741-X 0-470-90740-1 |
Formato | Materiale a stampa ![]() |
Livello bibliografico | Monografia |
Lingua di pubblicazione | eng |
Nota di contenuto | Biostatistics and Biomedical Science -- Relative Risk Estimates and Tests for Independent Groups -- Sample Size, Power, and Efficiency -- Stratified-Adjusted Analysis for Independent Groups -- Case-Control and Matched Studies -- Applications of Maximum Likelihood and Efficient Scores -- Logistic Regression Models -- Analysis of Count Data -- Analysis of Event-Time Data -- Appendix: Statistical Theory |
Record Nr. | UNINA-9910812397603321 |
Lachin John M. <1942->
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Hoboken, N.J., : Wiley, c2011 | ||
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Lo trovi qui: Univ. Federico II | ||
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Clinical Prediction Models : A Practical Approach to Development, Validation, and Updating / / by Ewout W. Steyerberg |
Autore | Steyerberg Ewout W |
Edizione | [2nd ed. 2019.] |
Pubbl/distr/stampa | Cham : , : Springer International Publishing : , : Imprint : Springer, , 2019 |
Descrizione fisica | 1 online resource (574 pages) |
Disciplina | 610.727 |
Collana | Statistics for Biology and Health |
Soggetto topico |
Medical statistics
Medicine - Research - Statistical methods Models, Statistical Regression Analysis |
ISBN | 3-030-16399-7 |
Formato | Materiale a stampa ![]() |
Livello bibliografico | Monografia |
Lingua di pubblicazione | eng |
Nota di contenuto | Introduction -- Applications of prediction models.Study design for prediction modeling -- Statistical Models for Prediction -- Overfitting and optimism in prediction models -- Choosing between alternative statistical models -- 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 information -- Evaluation of performance -- Evaluation of Clinical Usefulness -- Validation of Prediction Models -- Presentation formats -- Patterns of external validity -- Updating for a new setting -- Updating for multiple settings -- Case study on a prediction of 30-day mortality -- Case study on Survival Analysis: prediction of cardiovascular events -- Overall lessons and data sets -- References. |
Record Nr. | UNINA-9910349349103321 |
Steyerberg Ewout W
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Cham : , : Springer International Publishing : , : Imprint : Springer, , 2019 | ||
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Lo trovi qui: Univ. Federico II | ||
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